Methods for cell label classification

ABSTRACT

Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

RELATED APPLICATIONS

The present application is a divisional application of U.S. applicationSer. No. 15/806,174, filed Nov. 7, 2017, which claims priority under 35U.S.C. § 119(e) to U.S. Provisional Application No. 62/419,194, filed onNov. 8, 2016; and U.S. Provisional Application No. 62/445,546, filed onJan. 12, 2017. The content of each of these related applications isherein expressly incorporated by reference in its entirety.

BACKGROUND Field

The present disclosure relates generally to the field of molecularbarcoding and more particularly identifying and correcting noise celllabels.

Description of the Related Art

Methods and techniques such as stochastic barcoding are useful for cellanalysis, in particular deciphering gene expression profiles todetermine the states of cells using, for example, reverse transcription,polymerase chain reaction (PCR) amplification, and next generationsequencing (NGS). However, these methods and techniques can introduceerrors, if uncorrected, may result in overestimated cell counts.

SUMMARY

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) barcoding (e.g.,stochastically barcoding) a plurality of targets in a sample of cellsusing a plurality of barcodes (e.g., stochastic barcodes) to create aplurality of barcoded targets (e.g., stochastically barcoded targets),wherein each of the plurality of barcodes comprises a cell label and amolecular label; (b) obtaining sequencing data of the plurality ofbarcoded targets; (c) determining the number of molecular labels withdistinct sequences associated with each of the cell labels of theplurality of barcodes; (d) determining a rank of each of the cell labelsof the plurality of barcodes based on the number of molecular labelswith distinct sequences associated with each of the cell labels; (e)generating a cumulative sum plot based on the number of molecular labelswith distinct sequences associated with each of the cell labelsdetermined in (c) and the rank of each of the cell labels determined in(d); (f) generating a second derivative plot of the cumulative sum plot;(g) determining a minimum of the second derivative plot of thecumulative sum plot, wherein the minimum of the second derivative plotcorresponds to a cell label threshold; and (h) identifying each of thecell labels as a signal cell label or a noise cell label based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels determined in (c) and the cell label thresholddetermined in (g).

In some embodiments, the method comprises, if a cell label of theplurality of barcodes is identified as a noise cell label in (h),removing sequencing information associated with the identified celllabel from the sequencing data obtained in (b). The method can compriseremoving sequencing information associated with molecular labels withdistinct sequences associated with a target of the plurality of targetsfrom the sequencing data obtained in (b) if the number of the molecularlabels with distinct sequences associated with the target of theplurality of targets is above a molecular label occurrence threshold.

In some embodiments, wherein determining the number of molecular labelswith distinct sequences associated with each of the cell labels in (c)comprises removing sequencing information associated with non-uniquemolecular labels associated with each of the cell labels from thesequencing data. The cumulative sum plot can be a log-log plot. Thelog-log plot can be a log 10-log 10 plot.

In some embodiments, generating the cumulative sum plot based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels determined in (c) and the rank of each of the celllabels determined in (d) comprises: determining a cumulative sum foreach rank of the cell labels, wherein the cumulative sum for the rankcomprises a summation of a number of molecular labels with distinctsequences associated with each of the cell labels with a lower rank.Generating the second derivative plot of the cumulative sum plot cancomprise determining a difference between a cumulative sum of a firstrank of the cell labels and a cumulative sum of a second rank of thecell labels over a difference between the first rank and the secondrank. The difference between the first rank and the second rank can beone.

In some embodiments, the minimum is a global minimum. Determining theminimum of the second derivative plot comprises determining a minimum ofthe second derivative plot above a threshold of a minimum number ofmolecular labels associated with each of the cell labels.

In some embodiments, the threshold of the minimum number of molecularlabels associated with each of the cell labels is a percentilethreshold. The threshold of the minimum number of molecular labelsassociated with each of the cell labels is determined based on thenumber of cells in the sample of cells.

In some embodiments, determining the minimum of the second derivativeplot comprises determining a minimum of the second derivative plot belowa threshold of a maximum number of molecular labels associated with eachof the cell labels. The threshold of the maximum number of molecularlabels associated with each of the cell labels can be a percentilethreshold. The threshold of the maximum number of molecular labelsassociated with each of the cell labels can be determined based on thenumber of cells in the sample of cells.

In some embodiments, each of the cell labels is identified as the signalcell label if the number of molecular labels with distinct sequencesassociated with the each of the cell labels determined in (c) is greaterthan the cell label threshold. Each of the cell labels can be identifiedas a noise cell label if the number of molecular labels with distinctsequences associated with the each of the cell labels determined in (c)is not greater than the cell label threshold.

In some embodiments, the method comprises: (i) for one or more of theplurality of targets: (1) counting the number of molecular labels withdistinct sequences associated with the target in the sequencing data;and (2) estimating the number of the target based on the number ofmolecular labels with distinct sequences associated with the target inthe sequencing data counted in (1).

Disclosed herein are methods for determining a signal cell label. Insome embodiments, the method comprises: (a) obtaining sequencing data ofa plurality of barcoded targets (e.g., stochastically barcoded targets),wherein the plurality of barcoded targets is created from a plurality oftargets in a sample of cells that are barcoded (e.g., stochasticallybarcoded) using a plurality of barcodes (e.g., stochastic barcodes), andwherein each of the plurality of barcodes comprises a cell label and amolecular label; (b) determining a rank of each of the cell labels ofthe plurality of barcoded targets (or barcodes) based on the number ofmolecular labels with distinct sequences associated with each of thecell labels of the plurality of barcoded targets (or barcodes); (c)determining a cell label threshold based on the number of molecularlabels with distinct sequences associated with each of the cell labelsand the rank of each of the cell labels of the plurality of barcodedtargets (or barcodes) determined in (b); and identifying each of thecell labels as a signal cell label or a noise cell label based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels and the cell label threshold determined in (c).

In some embodiments, the method comprises determining the number ofmolecular labels with distinct sequences associated with each of thecell labels. Determining the number of molecular labels with distinctsequences associated with each of the cell labels can comprise removingsequencing information associated with non-unique molecular labelsassociated with the cell label from the sequencing data.

In some embodiments, determining the cell label threshold based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the plurality of barcoded targets comprises:determining the cell label with the largest change in a cumulative sumfor the cell label with a rank n and a cumulative sum for the cell labelwith the next rank n+1, wherein a number of molecular labels withdistinct sequences associated with the cell label corresponds to thecell label threshold.

In some embodiments, determining the cell label threshold based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the plurality of barcoded targets and the rank ofeach of the cell labels of the plurality of barcoded targets determinedin (b) comprises: determining a cumulative sum for each rank of the celllabels, wherein the cumulative sum for the rank comprises a summation ofa number of molecular labels with distinct sequences associated witheach of the cell labels with a lower rank; and determining a rank n ofthe cell labels with the largest change in a cumulative sum for the rankn and a cumulative sum for the next rank n+1, wherein the rank n of thecell labels with the largest change in the cumulative sum and thecumulative sum for the next rank n+1 corresponds to the cell labelthreshold.

In some embodiments, determining the cell label threshold based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the plurality of barcoded targets and the rank ofeach of the cell labels of the plurality of barcoded targets determinedin (b) comprises: generating a cumulative sum plot based on the numberof molecular labels with distinct sequences associated with each of thecell labels and the rank of each of the cell labels determined in (b);generating a second derivative plot of the cumulative sum plot; anddetermining a minimum of the second derivative plot of the cumulativesum plot, wherein the minimum of the second derivative plot correspondsto the cell label threshold. Generating the cumulative sum plot based onthe number of molecular labels with distinct sequences associated witheach of the cell labels and the rank of each of the cell labelsdetermined in (b) can comprise: determining a cumulative sum for eachrank of the cell labels, wherein the cumulative sum for the rankcomprises a summation of a number of molecular labels with distinctsequences associated with each of the cell labels with a lower rank.Generating the second derivative plot of the cumulative sum plot cancomprise determining a difference between a cumulative sum of a firstrank of the cell labels and a cumulative sum of a second rank of thecell labels over a difference between the first rank and the secondrank.

In some embodiments, the difference between the first rank and thesecond rank is one. In some embodiments, the method comprises removing,if a cell label of the plurality of barcoded targets is identified as anoise cell label in (d), sequencing information associated with theidentified cell label from the sequencing data obtained in (a). Themethod can comprise removing sequencing information associated withmolecular labels with distinct sequences associated with a target of theplurality of targets from the sequencing data obtained in (a) if thenumber of the molecular labels with distinct sequences associated withthe target of the plurality of targets is above a molecular labeloccurrence threshold. The cumulative sum plot can be a log-log plot. Thelog-log plot can be a log 10-log 10 plot.

In some embodiments, the minimum is a global minimum. Determining theminimum of the second derivative plot can comprise determining a minimumof the second derivative plot above a threshold of a minimum number ofmolecular labels associated with each of the cell labels. The thresholdof the minimum number of molecular labels associated with each of thecell labels can be a percentile threshold. The threshold of the minimumnumber of molecular labels associated with each of the cell labels canbe determined based on the number of cells in the sample of cells.

In some embodiments, determining the minimum of the second derivativeplot comprises determining a minimum of the second derivative plot belowa threshold of a maximum number of molecular labels associated with eachof the cell labels. The threshold of the maximum number of molecularlabels associated with each of the cell labels can be a percentilethreshold. The threshold of the maximum number of molecular labelsassociated with each of the cell labels can be determined based on thenumber of cells in the sample of cells.

In some embodiments, each of the cell labels is identified as the signalcell label if the number of molecular labels with distinct sequencesassociated with the each of the cell labels determined in (c) is greaterthan the cell label threshold. Each of the cell labels can be identifiedas a noise cell label if the number of molecular labels with distinctsequences associated with the each of the cell labels determined in (c)is not greater than the cell label threshold.

In some embodiments, the method comprises: (e) for one or more of theplurality of targets: (1) counting the number of molecular labels withdistinct sequences associated with the target in the sequencing data;and (2) estimating the number of the target based on the number ofmolecular labels with distinct sequences associated with the target inthe sequencing data counted in (1).

Disclosed herein are embodiments of a method for identifying a signalcell label. In some embodiments, the method comprises: (a) obtainingsequencing data of a plurality of targets of cells, wherein each targetis associated with a number of molecular labels with distinct sequencesassociated with each cell label of a plurality of cell labels; (b)determining a cell label threshold based on the number of molecularlabels with distinct sequences associated with each of the cell labels;and (c) identifying each of the cell labels as a signal cell label or anoise cell label based on the number of molecular labels with distinctsequences associated with each of the cell labels and the cell labelthreshold.

In some embodiments, obtaining sequencing data comprises: barcoding theplurality of targets of the cells using a plurality of barcodes tocreate a plurality of barcoded targets, wherein each of the plurality ofbarcodes comprises a cell label of the plurality of cell labels and amolecular label; and determining the number of molecular labels withdistinct sequences associated with each of the cell labels of theplurality of barcodes. In some embodiments, the method comprises: forone or more of the plurality of targets: (1) counting the number ofmolecular labels with distinct sequences associated with the target inthe sequencing data; and (2) estimating the number of the target basedon the number of molecular labels with distinct sequences associatedwith the target in the sequencing data counted in (1). The method cancomprise, if a cell label of the plurality of barcodes is identified asa noise cell label: removing sequencing information associated with theidentified cell label from the sequencing data. The method can comprise:removing sequencing information associated with molecular labels withdistinct sequences associated with a target of the plurality of targetsfrom the sequencing data if the number of the molecular labels withdistinct sequences associated with the target of the plurality oftargets is above a molecular label occurrence threshold. In someembodiments, determining the number of molecular labels with distinctsequences associated with each of the cell labels in (c) comprisesremoving sequencing information associated with non-unique molecularlabels associated with each of the cell labels from the sequencing data.

In some embodiments, determining the cell label threshold comprises:determining an inflection point of a cumulative sum plot, wherein thecumulative sum plot is based on the number of molecular labels withdistinct sequences associated with each of the plurality of cell labelsand a rank of each of the cell labels, and wherein the inflection pointcorresponds to the cell label threshold. Determining the inflectionpoint of the cumulative sum plot can comprise: generating the cumulativesum plot based on the number of molecular labels with distinct sequencesassociated with each of the plurality of cell labels and the rank ofeach of the cell labels; generating a second derivative plot of thecumulative sum plot; and determining a minimum of the second derivativeplot of the cumulative sum plot, wherein the minimum of the secondderivative plot corresponds to a cell label threshold. Determining thecell label threshold can comprise: determining the rank of each of theplurality of cell labels based on the number of molecular labels withdistinct sequences associated with each of the cell labels. Thecumulative sum plot can be a log-log plot, such as a log 10-log 10 plot.

In some embodiments, generating the cumulative sum plot based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels and the rank of each of the cell labels comprises:determining a cumulative sum for each rank of the cell labels, whereinthe cumulative sum for the rank comprises a summation of a number ofmolecular labels with distinct sequences associated with each of thecell labels with a lower rank. Generating the second derivative plot ofthe cumulative sum plot can comprise determining a difference between acumulative sum of a first rank of the cell labels and a cumulative sumof a second rank of the cell labels over a difference between the firstrank and the second rank. The difference between the first rank and thesecond rank can be one. The minimum can be a global minimum. Determiningthe minimum of the second derivative plot can comprise: determining aminimum of the second derivative plot above a threshold of a minimumnumber of molecular labels associated with each of the cell labels. Thethreshold of the minimum number of molecular labels associated with eachof the cell labels can be a percentile threshold. The threshold of theminimum number of molecular labels associated with each of the celllabels can be determined based on the number of the plurality of cells.

In some embodiments, determining the minimum of the second derivativeplot comprises determining a minimum of the second derivative plot belowa threshold of a maximum number of molecular labels associated with eachof the cell labels. The threshold of the maximum number of molecularlabels associated with each of the cell labels can be a percentilethreshold. The threshold of the maximum number of molecular labelsassociated with each of the cell labels can be determined based on thenumber of the plurality of cells.

In some embodiments, each of the cell labels can be identified as thesignal cell label if the number of molecular labels with distinctsequences associated with the each of the cell labels is greater thanthe cell label threshold. Each of the cell labels can be identified as anoise cell label if the number of molecular labels with distinctsequences associated with each of the cell labels is not greater thanthe cell label threshold.

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) barcoding (e.g.,stochastically barcoding) a plurality of targets in a sample of cellsusing a plurality of barcodes (e.g., stochastic barcodes) to create aplurality of barcoded targets (e.g., stochastically barcoded targets),wherein each of the plurality of barcodes comprises a cell label and amolecular label, wherein barcoded targets created from targets ofdifferent cells of the plurality of cells have different cell labels,and wherein barcoded targets created from targets of the same cell ofthe plurality of cells have different molecular labels; (b) obtainingsequencing data of the plurality of barcoded targets; (c) determining afeature vector of each cell label of the plurality of barcodes (orbarcoded targets), wherein the feature vector comprise numbers ofmolecular labels with distinct sequences associated with the each celllabel; (d) determining a cluster for the each cell label of theplurality of barcodes (or barcoded targets) based on the feature vector;and (e) identifying the each cell label of the plurality of stochasticbarcodes (or barcoded targets) as a signal cell label or a noise celllabel based on a number of cell labels in the cluster and a cluster sizethreshold.

In some embodiments, determining the cluster for the each cell label ofthe plurality of barcoded targets based on the feature vector comprisesclustering the each cell label of the plurality of barcoded targets intothe cluster based on a distance of the feature vector to the cluster ina feature vector space. Determining the cluster for each cell label ofthe plurality of barcoded targets based on the feature vector cancomprise: projecting the feature vector from a feature vector space intoa lower dimensional space; and clustering the each cell label into thecluster based on a distance of the feature vector to the cluster in thelower dimensional space.

In some embodiment, the lower dimensional space is a two dimensionalspace. Projecting the feature vector from the feature vector space intothe lower dimensional space can comprise projecting the feature vectorfrom the feature vector space into the lower dimensional space using at-distributed stochastic neighbor embedding (tSNE) method. Clusteringthe each cell label into the cluster based on the distance of thefeature vector to the cluster in the lower dimensional space cancomprise clustering the each cell label into the cluster based on thedistance of the feature vector to the cluster in the lower dimensionalspace using a density-based method. The density-based method cancomprise a density-based spatial clustering of applications with noise(DBSCAN) method.

In some embodiments, the cell label is identified as the signal celllabel if the number of cell labels in the cluster is below the clustersize threshold. The cell label can be identified as a noise cell labelif the number of cell labels in the cluster is not below the clustersize threshold. The method can comprise: (f) for one or more of theplurality of targets: (1) counting the number of molecular labels withdistinct sequences associated with the target in the sequencing data;and (2) estimating the number of the target based on the number ofmolecular labels with distinct sequences associated with the target inthe sequencing data counted in (1).

In some embodiments, the method comprises determining the cluster sizethreshold based on the number of cell labels of the plurality ofbarcoded targets. The cluster size threshold can be a percentage of thenumber of cell labels of the plurality of barcoded targets. In someembodiments, the method comprises determining the cluster size thresholdbased on the number of cell labels of the plurality of barcodes. Thecluster size threshold is a percentage of the number of cell labels ofthe plurality of barcodes. In some embodiments, the method comprisesdetermining the cluster size threshold based on numbers of molecularlabels with distinct sequences associated with each cell label of theplurality of barcodes.

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) obtaining sequencing data ofa plurality of barcoded targets (e.g., stochastically barcoded targets),wherein the plurality of barcoded targets is create from a plurality oftargets in a sample of cells that are barcoded (e.g., stochasticallybarcoded) using a plurality of barcodes (e.g., stochastic barcodes),wherein each of the plurality of barcodes comprises a cell label and amolecular label, wherein barcoded targets created from targets ofdifferent cells of the plurality of cells have different cell labels,and wherein barcoded targets created from targets of the same cell ofthe plurality of cells have different molecular labels; (b) determininga feature vector of each cell label of the plurality of barcodedtargets, wherein the feature vector comprise numbers of molecular labelswith distinct sequences associated with the each cell label; (c)determining a cluster for the each cell label of the plurality ofbarcoded targets based on the feature vector; and (d) identifying theeach cell label of the plurality of barcoded targets as a signal celllabel or a noise cell label based on a number of cell labels in thecluster and a cluster size threshold.

In some embodiments, determining the cluster for the each cell label ofthe plurality of barcoded targets based on the feature vector comprisesclustering the each cell label of the plurality of barcoded targets intothe cluster based on a distance of the feature vector to the cluster ina feature vector space. Determining the cluster for the each cell labelof the plurality of barcoded targets based on the feature vectorcomprises: projecting the feature vector from a feature vector spaceinto a lower dimensional space; and clustering the each cell label intothe cluster based on a distance of the feature vector to the cluster inthe lower dimensional space. The lower dimensional space can be a twodimensional space.

In some embodiments, projecting the feature vector from the featurevector space into the lower dimensional space comprises projecting thefeature vector from the feature vector space into the lower dimensionalspace using a t-distributed stochastic neighbor embedding (tSNE) method.Clustering the each cell label into the cluster based on the distance ofthe feature vector to the cluster in the lower dimensional space cancomprise clustering the each cell label into the cluster based on thedistance of the feature vector to the cluster in the lower dimensionalspace using a density-based method. The density-based method cancomprises a density-based spatial clustering of applications with noise(DBSCAN) method.

In some embodiments, the cell label can be identified as the signal celllabel if the number of cell labels in the cluster is below the clustersize threshold. The cell label can be identified as a noise cell labelif the number of cell labels in the cluster is not below the clustersize threshold.

In some embodiments, the method comprises determining the cluster sizethreshold based on the number of cell labels of the plurality ofbarcoded targets. The cluster size threshold can be a percentage of thenumber of cell labels of the plurality of barcoded targets. In someembodiments, determining the cluster size threshold based on the numberof cell labels of the plurality of barcodes. The cluster size thresholdcan be a percentage of the number of cell labels of the plurality ofbarcodes. In some embodiments, the method comprises determining thecluster size threshold based on numbers of molecular labels withdistinct sequences associated with each cell label of the plurality ofbarcodes.

In some embodiments, the method comprises: (e) for one or more of theplurality of targets: (1) counting the number of molecular labels withdistinct sequences associated with the target in the sequencing data;and (2) estimating the number of the target based on the number ofmolecular labels with distinct sequences associated with the target inthe sequencing data counted in (1).

Disclosed herein are embodiments of a method for identifying a signalcell label. In some embodiments, the method comprises: (a) obtainingsequencing data of a plurality of first targets of cells, wherein eachfirst target is associated with a number of molecular labels withdistinct sequences associated with each cell label of a plurality ofcell labels; (b) identifying each of the cell labels as a signal celllabel or a noise cell label based on the number of molecular labels withdistinct sequences associated with each of the cell labels and anidentification threshold; and (c) re-identifying at least one of theplurality of cell labels as a signal cell label identified as a noisecell label in (b) or re-identifying at least one of the cell label as anoise cell label identified as a signal cell label in (b). Identifyingeach of the cell labels, re-identifying at least one of the plurality ofcells labels as a signal cell label, or re-identifying at least one ofthe plurality of cell labels as a noise cell label can be based on anidentical cell label identification method or different cell labelidentification methods of the disclosure. The identification thresholdcan comprise a cell label threshold, a cluster size threshold, or anycombination thereof. The method can comprise: removing one or more celllabels of the plurality of cell labels each associated with a number ofmolecular labels with distinct sequences below threshold of a number ofmolecular labels.

In some embodiments, re-identifying at least one of the plurality ofcell labels as a signal cell label identified as a noise cell label in(b) comprises: determining a plurality of second targets of theplurality of first targets each with one or more variabilityindications, amongst the plurality of first targets, above a variabilitythreshold; and re-identifying at least one of the plurality of celllabels as a signal cell label identified as a noise cell label in (b)based on, for each of the plurality of cell labels, the number ofmolecular labels with distinct sequences associated with the pluralityof second targets and the identification threshold. The one or morevariability indications of the second target can comprise an average, amaximum, a median, a minimum, a dispersion, or any combinations thereof,of the numbers of molecular labels with distinct sequences associatedwith the second target and cell labels of the plurality of cell labelsin the sequencing data. The one or more variability indications of thesecond target can comprise a standard deviation, a normalizeddispersion, or any combinations thereof, variability indications of asubset of the plurality of second targets. The variability threshold canbe smaller than or equal to the size of the subset of the plurality ofsecond targets.

In some embodiments, re-identifying at least one of the plurality ofcell labels as a noise cell label identified as a signal cell label in(b) comprises: determining a plurality of third targets of the pluralityof first targets each with an association with cell labels identified asnoise cell labels in (c) above an association threshold; andre-identifying at least one of the cell label as a noise cell labelidentified as a signal cell label in (b), for each of the plurality ofcell labels, based on the number of molecular labels with distinctsequences associated with the plurality of third targets, and theidentification threshold. Determining the plurality of third targets ofthe plurality of first targets each with an association with cell labelsidentified as noise cell labels in (c) above the association thresholdcan comprise: determining a plurality of remaining cells labelsidentified as signal cell labels after re-identifying at least one ofthe cell label as a signal cell label identified as a noise cell labelin (b); determining the plurality of third targets based on for each ofthe plurality of cell labels, the number of molecular labels withdistinct sequences associated with the plurality of targets, and foreach of the plurality of remaining cell labels, the number of molecularlabels with distinct sequences associated with the plurality of targets.

Disclosed herein are systems for identifying a signal cell label. Insome embodiments, the system comprises: a hardware processor; andnon-transitory memory having instructions stored thereon, which whenexecuted by the hardware processor causes the processor to perform anyof the methods disclosed herein. Disclosed herein are computer readablemedia for identifying a signal cell label. In some embodiments, thecomputer readable medium comprises code for performing any of themethods disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a non-limiting exemplary barcode (e.g., a stochasticbarcode).

FIG. 2 shows a non-limiting exemplary workflow of barcoding and digitalcounting (e.g., stochastic barcoding and digital counting).

FIG. 3 is a schematic illustration showing a non-limiting exemplaryprocess for generating an indexed library of the barcoded targets (e.g.,stochastically barcoded targets) from a plurality of targets.

FIG. 4 is a flowchart showing a non-limiting exemplary method ofidentifying a cell as a signal cell label or a noise cell label.

FIG. 5 is a flowchart showing another non-limiting exemplary method ofidentifying a cell as a signal cell label or a noise cell label.

FIG. 6A is a flow diagram showing a non-limiting exemplary method fordistinguishing labels associated with true cells from noise cells. FIG.6B is a flow diagram showing another non-limiting exemplary method fordistinguishing labels associated with true cells from noise cells.

FIG. 7 is a non-limiting exemplary schematic illustration showingidentification of the most variable genes. A method for distinguishinglabels associated with true cells from noise cells (e.g., the method 600a described with reference to FIG. 6A, illustrated in Example 4) caninclude identification of the most variable genes.

FIGS. 8A-8B are non-limiting exemplary plots illustrating identificationof genes with biggest lose in the number of molecular labels withdistinct sequences associated for each gene. A method for distinguishinglabels associated with true cells from noise cells (e.g., the method 600a described with reference to FIG. 6A, illustrated in Example 4) caninclude identification of genes with biggest lose in the number ofmolecular labels associated with distinct sequences for each gene.

FIG. 9 is a block diagram of an illustrative computing system configuredto implement methods of the disclosure.

FIG. 10 shows a non-limiting exemplary cumulative sum plot.

FIG. 11 shows a non-limiting second derivative plot of the cumulativesum plot in FIG. 10 .

FIG. 12 shows a non-limiting tSNE plot of signal or noise cell labels.

FIGS. 13A-13B are non-limiting exemplary plots illustrating comparisonof cells identified by the method 400 illustrated with reference to FIG.4 (FIG. 13A) and the method 600 a illustrated with reference to FIG. 6A(FIG. 13B) for a sample processed using the BD™ Breast Cancer gene panelwith three distinct breast cancer cell lines and donor isolated PBMCs.The dots labeled as blue in both FIGS. 13A-13B are the common cellsdetected by both methods. The dots labeled as red in FIG. 13A are thecells identified as noise by method 600 a. The dots labeled as red inFIG. 13B are the additional true cells identified by method 600 a.

FIG. 14A is non-limiting exemplary plot showing the cells identified bythe method 600 a, where the cells labeled red are the additional cellsidentified (compared to the cells identified by method 400 illustratedwith reference to FIG. 4 ). The cells are colored by expression ofPBMCs, such as B cells (FIG. 14B), NK cells (FIG. 14C), and T cells(FIG. 14D). FIGS. 14B-14D show that the additional cells identified bythe method 600 a are indeed true cells.

FIG. 15A-15B are non-limiting exemplary plots illustrating comparison ofcells identified by the method 400 illustrated with reference to FIG. 4(FIG. 15A) and the method 600 a illustrated with reference to FIG. 6A(FIG. 15B) for a sample processed using the BD™ Blood gene panel with ahealthy donor isolated PBMCs. The dots labeled as blue in both FIGS.15A-15B are the common cells detected by both methods. The dots labeledas red in FIG. 15A are the cells identified as noise by the method 600a. The dots labeled as red in FIG. 15B are the additional cellsidentified by the method 600 a.

FIG. 16A-16B are non-limiting exemplary plots showing the cellsidentified by the method 400. In FIG. 16A, the cells labeled as red arethe cells identified as noise by the method 600 a. In FIG. 16B, thecells are colored by expression of a group of Monocyte marker genes,such as CD14 and S100A6. The “noise” cells identified by the improvedalgorithm were mostly low expressers of the Monocytes.

FIG. 17A is a non-limiting exemplary plot showing the cells identifiedby the method 600 a, where the cells labeled as are the additional cellsidentified. The cells are colored by expression of T cells (FIG. 17B),expression of important genes LAT (FIG. 17C) and IL7R (FIG. 17D).

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein and made part of the disclosure herein.

All patents, published patent applications, other publications, andsequences from GenBank, and other databases referred to herein areincorporated by reference in their entirety with respect to the relatedtechnology.

Quantifying small numbers of nucleic acids or targets, for examplemessenger ribonucleotide acid (mRNA) molecules, is clinically importantfor determining, for example, the genes that are expressed in a cell atdifferent stages of development or under different environmentalconditions. However, it can be very challenging to determine theabsolute number of nucleic acid molecules (e.g., mRNA molecules),especially when the number of molecules is very small. One method todetermine the absolute number of molecules in a sample is digitalpolymerase chain reaction (PCR). Ideally, PCR produces an identical copyof a molecule at each cycle. However, PCR can have disadvantages suchthat each molecule replicates with a stochastic probability, and thisprobability varies by PCR cycle and gene sequence, resulting inamplification bias and inaccurate gene expression measurements.

Barcodes (e.g., stochastic barcodes) with unique molecular labels (MLs,also referred to as molecular indexes (MIs)) can be used to count thenumbers of molecules. Barcodes with molecular labels that are unique foreach cell label can be used to count the numbers of molecules in eachcell. Non-limiting exemplary assays for barcoding include the Precis™assay (Cellular Research, Inc. (Palo Alto, Calif.)), the Resolve™ assay(Cellular Research, Inc. (Palo Alto, Calif.)), or the Rhapsody™ assay(Cellular Research, Inc. (Palo Alto, Calif.)). However, these methodsand techniques can introduce errors, if uncorrected, may result inoverestimated cell counts.

The Rhapsody™ assay can utilize a non-depleting pool of barcodes (e.g.,stochastic barcodes) with large number, for example 6561 to 65536,unique molecular labels on poly(T) oligonucleotides to hybridize to allpoly(A)-mRNAs in a sample during the RT step. In addition to molecularlabels, cell labels of the barcodes can be used to identify each singlecell in each well of a microwell plate. A barcode can comprise auniversal PCR priming site. During RT, target gene molecules reactrandomly with barcodes. Each target molecule can hybridize to a barcode(e.g., a stochastic barcode) resulting to generate barcodedcomplementary ribonucleotide acid (cDNA) molecules (e.g., stochasticallybarcoded cDNA molecules. After labeling, barcoded cDNA molecules frommicrowells of a microwell plate can be pooled into a single tube for PCRamplification and sequencing. Raw sequencing data can be analyzed toproduce the numbers of barcodes with unique molecular labels.

Methods and systems for identifying a signal cell label are disclosedherein. In some embodiments, the method comprises: (a) barcoding (e.g.,stochastically barcoding) a plurality of targets in a sample of cellsusing a plurality of barcodes (e.g., stochastic barcodes) to create aplurality of barcoded targets (e.g., stochastically barcoded targets),wherein each of the plurality of barcodes comprises a cell label and amolecular label; (b) obtaining sequencing data of the plurality ofbarcoded targets; (c) determining the number of molecular labels withdistinct sequences associated with each of the cell labels of theplurality of barcodes; (d) determining a rank of each of the cell labelsof the plurality of barcodes based on the number of molecular labelswith distinct sequences associated with each of the cell labels; (e)generating a cumulative sum plot based on the number of molecular labelswith distinct sequences associated with each of the cell labelsdetermined in (c) and the rank of each of the cell labels determined in(d); (f) generating a second derivative plot of the cumulative sum plot;(g) determining a minimum of the second derivative plot of thecumulative sum plot, wherein the minimum of the second derivative plotcorresponds to a cell label threshold; and (h) identifying the celllabel as a signal cell label or a noise cell label based on the numberof molecular labels with distinct sequences associated with the celllabel determined in (c) and the cell label threshold.

In some embodiments, the method comprises: (a) obtaining sequencing dataof a plurality of barcoded targets (e.g., stochastically barcodedtargets), wherein the sequencing data of the plurality of barcodedtargets are from a plurality of targets in a sample of cells that arebarcoded (e.g., stochastically barcoded) using a plurality of barcodes(e.g., stochastic barcodes) to create the plurality of barcoded targets(e.g., stochastically barcoded targets), wherein each of the pluralityof barcodes comprises a cell label and a molecular label; (b)determining a rank of each of the cell labels of the plurality ofbarcodes based on the number of molecular labels with distinct sequencesassociated with each of the cell labels; (c) determining a minimum of asecond derivative plot of a cumulative sum plot, wherein the cumulativesum plot is based on the number of molecular labels with distinctsequences associated with each of the cell labels and the rank of eachof the cell labels determined in (b), and wherein the minimum of thesecond derivative plot corresponds to a cell label threshold; and (d)identifying the cell label as a signal cell label (associated with acell) or a noise cell label (not associated with a cell based on thenumber of molecular labels with distinct sequences associated with thecell label and the cell label threshold.

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) barcoding (e.g.,stochastically barcoding) a plurality of targets in a sample of cellsusing a plurality of barcodes (e.g., stochastic barcodes) to create aplurality of barcoded targets (e.g., stochastically barcoded targets),wherein each of the plurality of barcodes comprises a cell label and amolecular label, wherein barcoded targets created from targets ofdifferent cells have different cell labels, and wherein barcoded targetscreated from targets of one cell of the plurality of cells havedifferent molecular labels; (b) obtaining sequencing data of thebarcoded targets; (c) determining a feature vector of the cell label,wherein the feature vector comprise the numbers of the molecular labelswith distinct sequences associated with the cell label; (d) determininga cluster for the cell label based on the feature vector; and (e)identifying the cell label as a signal cell label or a noise cell labelbased on the number of the cells in the cluster and a cluster sizethreshold.

Disclosed herein are systems for identifying a signal cell label. Insome embodiments, the system comprises: a hardware processor; andnon-transitory memory having instructions stored thereon, which whenexecuted by the hardware processor causes the processor to perform anyof the methods disclosed herein. Disclosed herein are computer readablemedia for identifying a signal cell label. In some embodiments, thecomputer readable medium comprises code for performing any of themethods disclosed herein.

Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the present disclosure belongs. See, e.g. Singleton etal., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley& Sons (New York, N.Y. 1994); Sambrook et al., Molecular Cloning, ALaboratory Manual, Cold Springs Harbor Press (Cold Springs Harbor, N Y1989). For purposes of the present disclosure, the following terms aredefined below.

As used herein, the term “adaptor” can mean a sequence to facilitateamplification or sequencing of associated nucleic acids. The associatednucleic acids can comprise target nucleic acids. The associated nucleicacids can comprise one or more of spatial labels, target labels, samplelabels, indexing label, barcodes, stochastic barcodes, or molecularlabels. The adapters can be linear. The adaptors can be pre-adenylatedadapters. The adaptors can be double- or single-stranded. One or moreadaptor can be located on the 5′ or 3′ end of a nucleic acid. When theadaptors comprise known sequences on the 5′ and 3′ ends, the knownsequences can be the same or different sequences. An adaptor located onthe 5′ and/or 3′ ends of a polynucleotide can be capable of hybridizingto one or more oligonucleotides immobilized on a surface. An adaptercan, in some embodiments, comprise a universal sequence. A universalsequence can be a region of nucleotide sequence that is common to two ormore nucleic acid molecules. The two or more nucleic acid molecules canhave regions of different sequence. Thus, for example, the 5′ adapterscan comprise identical and/or universal nucleic acid sequences and the3′ adapters can comprise identical and/or universal sequences. Auniversal sequence that may be present in different members of aplurality of nucleic acid molecules can allow the replication oramplification of multiple different sequences using a single universalprimer that is complementary to the universal sequence. Similarly, atleast one, two (e.g., a pair) or more universal sequences that may bepresent in different members of a collection of nucleic acid moleculescan allow the replication or amplification of multiple differentsequences using at least one, two (e.g., a pair) or more singleuniversal primers that are complementary to the universal sequences.Thus, a universal primer includes a sequence that can hybridize to sucha universal sequence. The target nucleic acid sequence-bearing moleculesmay be modified to attach universal adapters (e.g., non-target nucleicacid sequences) to one or both ends of the different target nucleic acidsequences. The one or more universal primers attached to the targetnucleic acid can provide sites for hybridization of universal primers.The one or more universal primers attached to the target nucleic acidcan be the same or different from each other.

As used herein the term “associated” or “associated with” can mean thattwo or more species are identifiable as being co-located at a point intime. An association can mean that two or more species are or werewithin a similar container. An association can be an informaticsassociation, where for example digital information regarding two or morespecies is stored and can be used to determine that one or more of thespecies were co-located at a point in time. An association can be aphysical association. In some embodiments, two or more associatedspecies are “tethered”, “attached”, or “immobilized” to one another orto a common solid or semisolid surface. An association may refer tocovalent or non-covalent means for attaching labels to solid orsemi-solid supports such as beads. An association may be a covalent bondbetween a target and a label.

As used herein, the term “complementary” can refer to the capacity forprecise pairing between two nucleotides. For example, if a nucleotide ata given position of a nucleic acid is capable of hydrogen bonding with anucleotide of another nucleic acid, then the two nucleic acids areconsidered to be complementary to one another at that position.Complementarity between two single-stranded nucleic acid molecules maybe “partial,” in which only some of the nucleotides bind, or it may becomplete when total complementarity exists between the single-strandedmolecules. A first nucleotide sequence can be said to be the“complement” of a second sequence if the first nucleotide sequence iscomplementary to the second nucleotide sequence. A first nucleotidesequence can be said to be the “reverse complement” of a secondsequence, if the first nucleotide sequence is complementary to asequence that is the reverse (i.e., the order of the nucleotides isreversed) of the second sequence. As used herein, the terms“complement”, “complementary”, and “reverse complement” can be usedinterchangeably. It is understood from the disclosure that if a moleculecan hybridize to another molecule it may be the complement of themolecule that is hybridizing.

As used herein, the term “digital counting” can refer to a method forestimating a number of target molecules in a sample. Digital countingcan include the step of determining a number of unique labels that havebeen associated with targets in a sample. This stochastic methodologytransforms the problem of counting molecules from one of locating andidentifying identical molecules to a series of yes/no digital questionsregarding detection of a set of predefined labels.

As used herein, the term “label” or “labels” can refer to nucleic acidcodes associated with a target within a sample. A label can be, forexample, a nucleic acid label. A label can be an entirely or partiallyamplifiable label. A label can be entirely or partially sequencablelabel. A label can be a portion of a native nucleic acid that isidentifiable as distinct. A label can be a known sequence. A label cancomprise a junction of nucleic acid sequences, for example a junction ofa native and non-native sequence. As used herein, the term “label” canbe used interchangeably with the terms, “index”, “tag,” or “label-tag.”Labels can convey information. For example, in various embodiments,labels can be used to determine an identity of a sample, a source of asample, an identity of a cell, and/or a target.

As used herein, the term “non-depleting reservoirs” can refer to a poolof stochastic barcodes made up of many different labels. A non-depletingreservoir can comprise large numbers of different stochastic barcodessuch that when the non-depleting reservoir is associated with a pool oftargets each target is likely to be associated with a unique stochasticbarcode. The uniqueness of each labeled target molecule can bedetermined by the statistics of random choice, and depends on the numberof copies of identical target molecules in the collection compared tothe diversity of labels. The size of the resulting set of labeled targetmolecules can be determined by the stochastic nature of the barcodingprocess, and analysis of the number of stochastic barcodes detected thenallows calculation of the number of target molecules present in theoriginal collection or sample. When the ratio of the number of copies ofa target molecule present to the number of unique stochastic barcodes islow, the labeled target molecules are highly unique (i.e. there is avery low probability that more than one target molecule will have beenlabeled with a given label).

As used herein, the term “nucleic acid” refers to a polynucleotidesequence, or fragment thereof. A nucleic acid can comprise nucleotides.A nucleic acid can be exogenous or endogenous to a cell. A nucleic acidcan exist in a cell-free environment. A nucleic acid can be a gene orfragment thereof. A nucleic acid can be DNA. A nucleic acid can be RNA.A nucleic acid can comprise one or more analogs (e.g. altered backbone,sugar, or nucleobase). Some non-limiting examples of analogs include:5-bromouracil, peptide nucleic acid, xeno nucleic acid, morpholinos,locked nucleic acids, glycol nucleic acids, threose nucleic acids,dideoxynucleotides, cordycepin, 7-deaza-GTP, fluorophores (e.g.rhodamine or fluorescein linked to the sugar), thiol containingnucleotides, biotin linked nucleotides, fluorescent base analogs, CpGislands, methyl-7-guanosine, methylated nucleotides, inosine,thiouridine, pseudouridine, dihydrouridine, queuosine, and wyosine.“Nucleic acid”, “polynucleotide, “target polynucleotide”, and “targetnucleic acid” can be used interchangeably.

A nucleic acid can comprise one or more modifications (e.g., a basemodification, a backbone modification), to provide the nucleic acid witha new or enhanced feature (e.g., improved stability). A nucleic acid cancomprise a nucleic acid affinity tag. A nucleoside can be a base-sugarcombination. The base portion of the nucleoside can be a heterocyclicbase. The two most common classes of such heterocyclic bases are thepurines and the pyrimidines. Nucleotides can be nucleosides that furtherinclude a phosphate group covalently linked to the sugar portion of thenucleoside. For those nucleosides that include a pentofuranosyl sugar,the phosphate group can be linked to the 2′, the 3′, or the 5′ hydroxylmoiety of the sugar. In forming nucleic acids, the phosphate groups cancovalently link adjacent nucleosides to one another to form a linearpolymeric compound. In turn, the respective ends of this linearpolymeric compound can be further joined to form a circular compound;however, linear compounds are generally suitable. In addition, linearcompounds may have internal nucleotide base complementarity and maytherefore fold in a manner as to produce a fully or partiallydouble-stranded compound. Within nucleic acids, the phosphate groups cancommonly be referred to as forming the internucleoside backbone of thenucleic acid. The linkage or backbone can be a 3′ to 5′ phosphodiesterlinkage.

A nucleic acid can comprise a modified backbone and/or modifiedinternucleoside linkages. Modified backbones can include those thatretain a phosphorus atom in the backbone and those that do not have aphosphorus atom in the backbone. Suitable modified nucleic acidbackbones containing a phosphorus atom therein can include, for example,phosphorothioates, chiral phosphorothioates, phosphorodithioates,phosphotriesters, aminoalkyl phosphotriesters, methyl and other alkylphosphonate such as 3′-alkylene phosphonates, 5′-alkylene phosphonates,chiral phosphonates, phosphinates, phosphoramidates including 3′-aminophosphoramidate and aminoalkyl phosphoramidates, phosphorodiamidates,thionophosphoramidates, thionoalkylphosphonates,thionoalkylphosphotriesters, selenophosphates, and boranophosphateshaving normal 3′-5′ linkages, 2′-5′ linked analogs, and those havinginverted polarity wherein one or more internucleotide linkages is a 3′to 3′, a 5′ to 5′ or a 2′ to 2′ linkage.

A nucleic acid can comprise polynucleotide backbones that are formed byshort chain alkyl or cycloalkyl internucleoside linkages, mixedheteroatom and alkyl or cycloalkyl internucleoside linkages, or one ormore short chain heteroatomic or heterocyclic internucleoside linkages.These can include those having morpholino linkages (formed in part fromthe sugar portion of a nucleoside); siloxane backbones; sulfide,sulfoxide and sulfone backbones; formacetyl and thioformacetylbackbones; methylene formacetyl and thioformacetyl backbones; riboacetylbackbones; alkene containing backbones; sulfamate backbones;methyleneimino and methylenehydrazino backbones; sulfonate andsulfonamide backbones; amide backbones; and others having mixed N, O, Sand CH2 component parts.

A nucleic acid can comprise a nucleic acid mimetic. The term “mimetic”can be intended to include polynucleotides wherein only the furanosering or both the furanose ring and the internucleotide linkage arereplaced with non-furanose groups, replacement of only the furanose ringcan be referred as being a sugar surrogate. The heterocyclic base moietyor a modified heterocyclic base moiety can be maintained forhybridization with an appropriate target nucleic acid. One such nucleicacid can be a peptide nucleic acid (PNA). In a PNA, the sugar-backboneof a polynucleotide can be replaced with an amide containing backbone,in particular an aminoethylglycine backbone. The nucleotides can beretained and are bound directly or indirectly to aza nitrogen atoms ofthe amide portion of the backbone. The backbone in PNA compounds cancomprise two or more linked aminoethylglycine units which gives PNA anamide containing backbone. The heterocyclic base moieties can be bounddirectly or indirectly to aza nitrogen atoms of the amide portion of thebackbone.

A nucleic acid can comprise a morpholino backbone structure. Forexample, a nucleic acid can comprise a 6-membered morpholino ring inplace of a ribose ring. In some of these embodiments, aphosphorodiamidate or other non-phosphodiester internucleoside linkagecan replace a phosphodiester linkage.

A nucleic acid can comprise linked morpholino units (i.e. morpholinonucleic acid) having heterocyclic bases attached to the morpholino ring.Linking groups can link the morpholino monomeric units in a morpholinonucleic acid. Non-ionic morpholino-based oligomeric compounds can haveless undesired interactions with cellular proteins. Morpholino-basedpolynucleotides can be nonionic mimics of nucleic acids. A variety ofcompounds within the morpholino class can be joined using differentlinking groups. A further class of polynucleotide mimetic can bereferred to as cyclohexenyl nucleic acids (CeNA). The furanose ringnormally present in a nucleic acid molecule can be replaced with acyclohexenyl ring. CeNA DMT protected phosphoramidite monomers can beprepared and used for oligomeric compound synthesis usingphosphoramidite chemistry. The incorporation of CeNA monomers into anucleic acid chain can increase the stability of a DNA/RNA hybrid. CeNAoligoadenylates can form complexes with nucleic acid complements withsimilar stability to the native complexes. A further modification caninclude Locked Nucleic Acids (LNAs) in which the 2′-hydroxyl group islinked to the 4′ carbon atom of the sugar ring thereby forming a 2′-C,4′-C-oxymethylene linkage thereby forming a bicyclic sugar moiety. Thelinkage can be a methylene (—CH2-), group bridging the 2′ oxygen atomand the 4′ carbon atom wherein n is 1 or 2. LNA and LNA analogs candisplay very high duplex thermal stabilities with complementary nucleicacid (Tm=+3 to +10° C.), stability towards 3′-exonucleolytic degradationand good solubility properties.

A nucleic acid may also include nucleobase (often referred to simply as“base”) modifications or substitutions. As used herein, “unmodified” or“natural” nucleobases can include the purine bases, (e.g. adenine (A)and guanine (G)), and the pyrimidine bases, (e.g. thymine (T), cytosine(C) and uracil (U)). Modified nucleobases can include other syntheticand natural nucleobases such as 5-methylcytosine (5-me-C),5-hydroxymethyl cytosine, xanthine, hypoxanthine, 2-aminoadenine,6-methyl and other alkyl derivatives of adenine and guanine, 2-propyland other alkyl derivatives of adenine and guanine, 2-thiouracil,2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl(—C═C—CH3) uracil and cytosine and other alkynyl derivatives ofpyrimidine bases, 6-azo uracil, cytosine and thymine, 5-uracil(pseudouracil), 4-thiouracil, 8-halo, 8-amino, 8-thiol, 8-thioalkyl,8-hydroxyl and other 8-substituted adenines and guanines, 5-haloparticularly 5-bromo, 5-trifluoromethyl and other 5-substituted uracilsand cytosines, 7-methylguanine and 7-methyladenine, 2-F-adenine,2-aminoadenine, 8-azaguanine and 8-azaadenine, 7-deazaguanine and7-deazaadenine and 3-deazaguanine and 3-deazaadenine. Modifiednucleobases can include tricyclic pyrimidines such as phenoxazinecytidine(1H-pyrimido(5,4-b)(1,4)benzoxazin-2(3H)-one), phenothiazinecytidine (1H-pyrimido(5,4-b)(1,4)benzothiazin-2(3H)-one), G-clamps suchas a substituted phenoxazine cytidine (e.g.9-(2-aminoethoxy)-H-pyrimido(5,4-(b) (1,4)benzoxazin-2(3H)-one),phenothiazine cytidine (1H-pyrimido(5,4-b)(1,4)benzothiazin-2(3H)-one),G-clamps such as a substituted phenoxazine cytidine (e.g.9-(2-aminoethoxy)-H-pyrimido(5,4-(b) (1,4)benzoxazin-2(3H)-one),carbazole cytidine (2H-pyrimido(4,5-b)indol-2-one), pyridoindolecytidine (H-pyrido(3 ‘,2’: 4, 5)pyrrolo[2,3-d]pyrimidin-2-one).

As used herein, the term “sample” can refer to a composition comprisingtargets. Suitable samples for analysis by the disclosed methods,devices, and systems include cells, tissues, organs, or organisms.

As used herein, the term “sampling device” or “device” can refer to adevice which may take a section of a sample and/or place the section ona substrate. A sample device can refer to, for example, a fluorescenceactivated cell sorting (FACS) machine, a cell sorter machine, a biopsyneedle, a biopsy device, a tissue sectioning device, a microfluidicdevice, a blade grid, and/or a microtome.

As used herein, the term “solid support” can refer to discrete solid orsemi-solid surfaces to which a plurality of stochastic barcodes may beattached. A solid support may encompass any type of solid, porous, orhollow sphere, ball, bearing, cylinder, or other similar configurationcomposed of plastic, ceramic, metal, or polymeric material (e.g.,hydrogel) onto which a nucleic acid may be immobilized (e.g., covalentlyor non-covalently). A solid support may comprise a discrete particlethat may be spherical (e.g., microspheres) or have a non-spherical orirregular shape, such as cubic, cuboid, pyramidal, cylindrical, conical,oblong, or disc-shaped, and the like. A plurality of solid supportsspaced in an array may not comprise a substrate. A solid support may beused interchangeably with the term “bead.”

A solid support can refer to a “substrate.” A substrate can be a type ofsolid support. A substrate can refer to a continuous solid or semi-solidsurface on which the methods of the disclosure may be performed. Asubstrate can refer to an array, a cartridge, a chip, a device, and aslide, for example.

As used here, the term, “spatial label” can refer to a label which canbe associated with a position in space.

As used herein, the term “stochastic barcode” can refer to apolynucleotide sequence comprising labels. A stochastic barcode can be apolynucleotide sequence that can be used for stochastic barcoding.Stochastic barcodes can be used to quantify targets within a sample.Stochastic barcodes can be used to control for errors which may occurafter a label is associated with a target. For example, a stochasticbarcode can be used to assess amplification or sequencing errors. Astochastic barcode associated with a target can be called a stochasticbarcode-target or stochastic barcode-tag-target.

As used herein, the term “gene-specific stochastic barcode” can refer toa polynucleotide sequence comprising labels and a target-binding regionthat is gene-specific. A stochastic barcode can be a polynucleotidesequence that can be used for stochastic barcoding. Stochastic barcodescan be used to quantify targets within a sample. Stochastic barcodes canbe used to control for errors which may occur after a label isassociated with a target. For example, a stochastic barcode can be usedto assess amplification or sequencing errors. A stochastic barcodeassociated with a target can be called a stochastic barcode-target orstochastic barcode-tag-target.

As used herein, the term “stochastic barcoding” can refer to the randomlabeling (e.g., barcoding) of nucleic acids. Stochastic barcoding canutilize a recursive Poisson strategy to associate and quantify labelsassociated with targets. As used herein, the term “stochastic barcoding”can be used interchangeably with “gene-specific stochastic barcoding.”

As used here, the term “target” can refer to a composition which can beassociated with a stochastic barcode. Exemplary suitable targets foranalysis by the disclosed methods, devices, and systems includeoligonucleotides, DNA, RNA, mRNA, microRNA, tRNA, and the like. Targetscan be single or double stranded. In some embodiments targets can beproteins. In some embodiments targets are lipids.

As used herein, the term “reverse transcriptases” can refer to a groupof enzymes having reverse transcriptase activity (i.e., that catalyzesynthesis of DNA from an RNA template). In general, such enzymesinclude, but are not limited to, retroviral reverse transcriptase,retrotransposon reverse transcriptase, retroplasmid reversetranscriptases, retron reverse transcriptases, bacterial reversetranscriptases, group II intron-derived reverse transcriptase, andmutants, variants or derivatives thereof. Non-retroviral reversetranscriptases include non-LTR retrotransposon reverse transcriptases,retroplasmid reverse transcriptases, retron reverse transciptases, andgroup II intron reverse transcriptases. Examples of group II intronreverse transcriptases include the Lactococcus lactis LI.LtrB intronreverse transcriptase, the Thermosynechococcus elongatus TeI4c intronreverse transcriptase, or the Geobacillus stearothermophilus GsI-IICintron reverse transcriptase. Other classes of reverse transcriptasescan include many classes of non-retroviral reverse transcriptases (i.e.,retrons, group II introns, and diversity-generating retroelements amongothers).

Disclosed herein are systems and methods for identifying a signal celllabel. In some embodiments, the method comprises: (a) stochasticallybarcoding a plurality of targets in a sample of cells using a pluralityof stochastic barcodes to create a plurality of stochastically barcodedtargets, wherein each of the plurality of stochastic barcodes comprisesa cell label and a molecular label; (b) obtaining sequencing data of theplurality of stochastically barcoded targets; (c) determining the numberof molecular labels with distinct sequences associated with each of thecell labels of the plurality of stochastic barcodes; (d) determining arank of each of the cell labels of the plurality of stochastic barcodesbased on the number of molecular labels with distinct sequencesassociated with each of the cell labels; (e) generating a cumulative sumplot based on the number of molecular labels with distinct sequencesassociated with each of the cell labels determined in (c) and the rankof each of the cell labels determined in (d); (f) generating a secondderivative plot of the cumulative sum plot; (g) determining a minimum ofthe second derivative plot of the cumulative sum plot, wherein theminimum of the second derivative plot corresponds to a cell labelthreshold; and (h) identifying each of the cell labels as a signal celllabel or a noise cell label based on the number of molecular labels withdistinct sequences associated with each of the cell labels determined in(c) and the cell label threshold determined in (g).

Barcodes

Barcoding, such as stochastic barcoding, has been described in, forexample, US20150299784, WO2015031691, and Fu et al, Proc Natl Acad SciU.S.A. 2011 May 31; 108(22):9026-31 and Fan et al., Science (2015)347(6222):1258367; the content of these publications is incorporatedhereby in its entirety. In some embodiments, the barcode disclosedherein can be a stochastic barcode which can be a polynucleotidesequence that may be used to stochastically label (e.g., barcode, tag) atarget. Barcodes can be referred to stochastic barcodes if the ratio ofthe number of different barcode sequences of the stochastic barcodes andthe number of occurrence of any of the targets to be labeled can be, orabout, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1,13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1,70:1, 80:1, 90:1, 100:1, or a number or a range between any two of thesevalues. A target can be, for example, an mRNA species comprising mRNAmolecules with identical or nearly identical sequences. Barcodes can bereferred to as stochastic barcodes if the ratio of the number ofdifferent barcode sequences of the stochastic barcodes and the number ofoccurrence of any of the targets to be labeled is at least, or at most,1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1,14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1,80:1, 90:1, or 100:1. Barcode sequences of stochastic barcodes can bereferred to as molecular labels.

A barcode, for example a stochastic barcode, can comprise one or morelabels. Exemplary labels can include a universal label, a cell label, abarcode sequence (e.g., a molecular label), a sample label, a platelabel, a spatial label, and/or a pre-spatial label. FIG. 1 illustratesan exemplary barcode 104 with a spatial label. The barcode 104 cancomprise a 5′ amine that may link the barcode to a solid support 105.The barcode can comprise a universal label, a dimension label, a spatiallabel, a cell label, and/or a molecular label. The order of differentlabels (including but not limited to the universal label, the dimensionlabel, the spatial label, the cell label, and the molecule label) in thebarcode can vary. For example, as shown in FIG. 1 , the universal labelmay be the 5′-most label, and the molecular label may be the 3′-mostlabel. The spatial label, dimension label, and the cell label may be inany order. In some embodiments, the universal label, the spatial label,the dimension label, the cell label, and the molecular label are in anyorder. The barcode can comprise a target-binding region. Thetarget-binding region can interact with a target (e.g., target nucleicacid, RNA, mRNA, DNA) in a sample. For example, a target-binding regioncan comprise an oligo(dT) sequence which can interact with poly(A) tailsof mRNAs. In some instances, the labels of the barcode (e.g., universallabel, dimension label, spatial label, cell label, and barcode sequence)may be separated by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, or 20 or more nucleotides.

A label, for example the cell label, can comprise a unique set ofnucleic acid sub-sequences of defined length, e.g. seven nucleotideseach (equivalent to the number of bits used in some Hamming errorcorrection codes), which can be designed to provide error correctioncapability. The set of error correction sub-sequences comprise sevennucleotide sequences can be designed such that any pairwise combinationof sequences in the set exhibits a defined “genetic distance” (or numberof mismatched bases), for example, a set of error correctionsub-sequences can be designed to exhibit a genetic distance of threenucleotides. In this case, review of the error correction sequences inthe set of sequence data for labeled target nucleic acid molecules(described more fully below) can allow one to detect or correctamplification or sequencing errors. In some embodiments, the length ofthe nucleic acid sub-sequences used for creating error correction codescan vary, for example, they can be, or be about 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 15, 20, 30, 31, 40, 50, or a number or a range between any two ofthese values, nucleotides in length. In some embodiments, nucleic acidsub-sequences of other lengths can be used for creating error correctioncodes.

The barcode can comprise a target-binding region. The target-bindingregion can interact with a target in a sample. The target can be, orcomprise, ribonucleic acids (RNAs), messenger RNAs (mRNAs), microRNAs,small interfering RNAs (siRNAs), RNA degradation products, RNAs eachcomprising a poly(A) tail, or any combination thereof. In someembodiments, the plurality of targets can include deoxyribonucleic acids(DNAs).

In some embodiments, a target-binding region can comprise an oligo(dT)sequence which can interact with poly(A) tails of mRNAs. One or more ofthe labels of the barcode (e.g., the universal label, the dimensionlabel, the spatial label, the cell label, and the barcode sequence(e.g., a molecular label)) can be separated by a spacer from another oneor two of the remaining labels of the barcode. The spacer can be, forexample, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, or 20 or more nucleotides. In some embodiments, none of the labelsof the barcode is separated by spacer.

Universal Labels

A barcode can comprise one or more universal labels. In someembodiments, the one or more universal labels can be the same for allbarcodes in the set of barcodes attached to a given solid support. Insome embodiments, the one or more universal labels can be the same forall barcodes attached to a plurality of beads. In some embodiments, auniversal label can comprise a nucleic acid sequence that is capable ofhybridizing to a sequencing primer. Sequencing primers can be used forsequencing barcodes comprising a universal label. Sequencing primers(e.g., universal sequencing primers) can comprise sequencing primersassociated with high-throughput sequencing platforms. In someembodiments, a universal label can comprise a nucleic acid sequence thatis capable of hybridizing to a PCR primer. In some embodiments, theuniversal label can comprise a nucleic acid sequence that is capable ofhybridizing to a sequencing primer and a PCR primer. The nucleic acidsequence of the universal label that is capable of hybridizing to asequencing or PCR primer can be referred to as a primer binding site. Auniversal label can comprise a sequence that can be used to initiatetranscription of the barcode. A universal label can comprise a sequencethat can be used for extension of the barcode or a region within thebarcode. A universal label can be, or be about, 1, 2, 3, 4, 5, 10, 15,20, 25, 30, 35, 40, 45, 50, or a number or a range between any two ofthese values, nucleotides in length. For example, a universal label cancomprise at least about 10 nucleotides. A universal label can be atleast, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50,100, 200, or 300 nucleotides in length. In some embodiments, a cleavablelinker or modified nucleotide can be part of the universal labelsequence to enable the barcode to be cleaved off from the support.

Dimension Labels

A barcode can comprise one or more dimension labels. In someembodiments, a dimension label can comprise a nucleic acid sequence thatprovides information about a dimension in which the labeling (e.g.,stochastic labeling) occurred. For example, a dimension label canprovide information about the time at which a target was stochasticallybarcoded. A dimension label can be associated with a time of barcoding(e.g., stochastic barcoding) in a sample. A dimension label can beactivated at the time of labeling. Different dimension labels can beactivated at different times. The dimension label provides informationabout the order in which targets, groups of targets, and/or samples werestochastically barcoded. For example, a population of cells can bestochastically barcoded at the G0 phase of the cell cycle. The cells canbe pulsed again with barcodes (e.g., stochastic barcodes) at the G1phase of the cell cycle. The cells can be pulsed again with barcodes atthe S phase of the cell cycle, and so on. Barcodes at each pulse (e.g.,each phase of the cell cycle), can comprise different dimension labels.In this way, the dimension label provides information about whichtargets were labelled at which phase of the cell cycle. Dimension labelscan interrogate many different biological times. Exemplary biologicaltimes can include, but are not limited to, the cell cycle, transcription(e.g., transcription initiation), and transcript degradation. In anotherexample, a sample (e.g., a cell, a population of cells) can bestochastically labeled before and/or after treatment with a drug and/ortherapy. The changes in the number of copies of distinct targets can beindicative of the sample's response to the drug and/or therapy.

A dimension label can be activatable. An activatable dimension label canbe activated at a specific time point. The activatable label can be, forexample, constitutively activated (e.g., not turned off). Theactivatable dimension label can be, for example, reversibly activated(e.g., the activatable dimension label can be turned on and turned off).The dimension label can be, for example, reversibly activatable at least1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more times. The dimension label canbe reversibly activatable, for example, at least 1, 2, 3, 4, 5, 6, 7, 8,9, or 10 or more times. In some embodiments, the dimension label can beactivated with fluorescence, light, a chemical event (e.g., cleavage,ligation of another molecule, addition of modifications (e.g.,pegylated, sumoylated, acetylated, methylated, deacetylated,demethylated), a photochemical event (e.g., photocaging), andintroduction of a non-natural nucleotide.

The dimension label can, in some embodiments, be identical for allbarcodes (e.g., stochastic barcodes) attached to a given solid support(e.g., bead), but different for different solid supports (e.g., beads).In some embodiments, at least 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99% or100% of barcodes on the same solid support can comprise the samedimension label. In some embodiments, at least 60% of barcodes on thesame solid support can comprise the same dimension label. In someembodiments, at least 95% of barcodes on the same solid support cancomprise the same dimension label.

There can be as many as 10⁶ or more unique dimension label sequencesrepresented in a plurality of solid supports (e.g., beads). A dimensionlabel can be, or be about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45,50, or a number or a range between any two of these values, nucleotidesin length. A dimension label can be at least, or at most, 1, 2, 3, 4, 5,10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides inlength. A dimension label can comprise between about 5 to about 200nucleotides. A dimension label can comprise between about 10 to about150 nucleotides. A dimension label can comprise between about 20 toabout 125 nucleotides in length.

Spatial Labels

A barcode can comprise one or more spatial labels. In some embodiments,a spatial label can comprise a nucleic acid sequence that providesinformation about the spatial orientation of a target molecule which isassociated with the barcode. A spatial label can be associated with acoordinate in a sample. The coordinate can be a fixed coordinate. Forexample a coordinate can be fixed in reference to a substrate. A spatiallabel can be in reference to a two or three-dimensional grid. Acoordinate can be fixed in reference to a landmark. The landmark can beidentifiable in space. A landmark can be a structure which can beimaged. A landmark can be a biological structure, for example ananatomical landmark. A landmark can be a cellular landmark, for instancean organelle. A landmark can be a non-natural landmark such as astructure with an identifiable identifier such as a color code, barcode, magnetic property, fluorescents, radioactivity, or a unique sizeor shape. A spatial label can be associated with a physical partition(e.g. a well, a container, or a droplet). In some embodiments, multiplespatial labels are used together to encode one or more positions inspace.

The spatial label can be identical for all barcodes attached to a givensolid support (e.g., bead), but different for different solid supports(e.g., beads). In some embodiments, the percentage of barcodes on thesame solid support comprising the same spatial label can be, or beabout, 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or arange between any two of these values. In some embodiments, thepercentage of barcodes on the same solid support comprising the samespatial label can be at least, or at most, 60%, 70%, 80%, 85%, 90%, 95%,97%, 99%, or 100%. In some embodiments, at least 60% of barcodes on thesame solid support can comprise the same spatial label. In someembodiments, at least 95% of barcodes on the same solid support cancomprise the same spatial label.

There can be as many as 10⁶ or more unique spatial label sequencesrepresented in a plurality of solid supports (e.g., beads). A spatiallabel can be, or be about, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40,45, 50, or a number or a range between any two of these values,nucleotides in length. A spatial label can be at least or at most 1, 2,3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300nucleotides in length. A spatial label can comprise between about 5 toabout 200 nucleotides. A spatial label can comprise between about 10 toabout 150 nucleotides. A spatial label can comprise between about 20 toabout 125 nucleotides in length.

Cell Labels

A barcode can comprise one or more cell labels. In some embodiments, acell label can comprise a nucleic acid sequence that providesinformation for determining which target nucleic acid originated fromwhich cell. In some embodiments, the cell label is identical for allbarcodes attached to a given solid support (e.g., bead), but differentfor different solid supports (e.g., beads). In some embodiments, thepercentage of barcodes on the same solid support comprising the samecell label can be, or be about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%,100%, or a number or a range between any two of these values. In someembodiments, the percentage of barcodes on the same solid supportcomprising the same cell label can be, or be about 60%, 70%, 80%, 85%,90%, 95%, 97%, 99%, or 100%. For example, at least 60% of barcodes onthe same solid support can comprise the same cell label. As anotherexample, at least 95% of barcodes on the same solid support can comprisethe same cell label.

There can be as many as 10⁶ or more unique cell label sequencesrepresented in a plurality of solid supports (e.g., beads). A cell labelcan be, or be about, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50,or a number or a range between any two of these values, nucleotides inlength. A cell label can be at least, or at most, 1, 2, 3, 4, 5, 10, 15,20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides in length. Forexample, a cell label can comprise between about 5 to about 200nucleotides. As another example, a cell label can comprise between about10 to about 150 nucleotides. As yet another example, a cell label cancomprise between about 20 to about 125 nucleotides in length.

Barcode Sequences

A barcode can comprise one or more barcode sequences. In someembodiments, a barcode sequence can comprise a nucleic acid sequencethat provides identifying information for the specific type of targetnucleic acid species hybridized to the barcode. A barcode sequence cancomprise a nucleic acid sequence that provides a counter (e.g., thatprovides a rough approximation) for the specific occurrence of thetarget nucleic acid species hybridized to the barcode (e.g.,target-binding region).

In some embodiments, a diverse set of barcode sequences are attached toa given solid support (e.g., bead). In some embodiments, there can be,or be about, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, or a number or arange between any two of these values, unique molecular label sequences.For example, a plurality of barcodes can comprise about 6561 barcodessequences with distinct sequences. As another example, a plurality ofbarcodes can comprise about 65536 barcode sequences with distinctsequences. In some embodiments, there can be at least, or at most, 10²,10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, or 10⁹, unique barcode sequences. Theunique molecular label sequences can be attached to a given solidsupport (e.g., bead).

A barcode can be, or be about, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35,40, 45, 50, or a number or a range between any two of these values,nucleotides in length. A barcode can be at least, or at most, 1, 2, 3,4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotidesin length.

Molecular Labels

A stochastic barcode can comprise one or more molecular labels.Molecular labels can include barcode sequences. In some embodiments, amolecular label can comprise a nucleic acid sequence that providesidentifying information for the specific type of target nucleic acidspecies hybridized to the stochastic barcode. A molecular label cancomprise a nucleic acid sequence that provides a counter for thespecific occurrence of the target nucleic acid species hybridized to thestochastic barcode (e.g., target-binding region).

In some embodiments, a diverse set of molecular labels are attached to agiven solid support (e.g., bead). In some embodiments, there can be, orbe about, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, or a number or a rangeof unique molecular label sequences. For example, a plurality ofstochastic barcodes can comprise about 6561 molecular labels withdistinct sequences. As another example, a plurality of stochasticbarcodes can comprise about 65536 molecular labels with distinctsequences. In some embodiments, there can be at least, or at most, 10²,10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, or 10⁹, unique molecular label sequences.Stochastic barcodes with the unique molecular label sequences can beattached to a given solid support (e.g., bead).

For stochastic barcoding using a plurality of stochastic barcodes, theratio of the number of different molecular label sequences and thenumber of occurrence of any of the targets can be, or about, 1:1, 2:1,3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1,16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1,100:1, or a number or a range between any two of these values. A targetcan be an mRNA species comprising mRNA molecules with identical ornearly identical sequences. In some embodiments, the ratio of the numberof different molecular label sequences and the number of occurrence ofany of the targets is at least, or at most, 1:1, 2:1, 3:1, 4:1, 5:1,6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1,18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, or 100:1.

A molecular label can be, or be about, 1, 2, 3, 4, 5, 10, 15, 20, 25,30, 35, 40, 45, 50, or a number or a range between any two of thesevalues, nucleotides in length. A molecular label can be at least, or atmost, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or300 nucleotides in length.

Target-Binding Region

A barcode can comprise one or more target binding regions, such ascapture probes. In some embodiments, a target-binding region canhybridize with a target of interest. In some embodiments, the targetbinding regions can comprise a nucleic acid sequence that hybridizesspecifically to a target (e.g. target nucleic acid, target molecule,e.g., a cellular nucleic acid to be analyzed), for example to a specificgene sequence. In some embodiments, a target binding region can comprisea nucleic acid sequence that can attach (e.g., hybridize) to a specificlocation of a specific target nucleic acid. In some embodiments, thetarget binding region can comprise a nucleic acid sequence that iscapable of specific hybridization to a restriction enzyme site overhang(e.g. an EcoRI sticky-end overhang). The barcode can then ligate to anynucleic acid molecule comprising a sequence complementary to therestriction site overhang.

In some embodiments, a target binding region can comprise a non-specifictarget nucleic acid sequence. A non-specific target nucleic acidsequence can refer to a sequence that can bind to multiple targetnucleic acids, independent of the specific sequence of the targetnucleic acid. For example, target binding region can comprise a randommultimer sequence, or an oligo(dT) sequence that hybridizes to thepoly(A) tail on mRNA molecules. A random multimer sequence can be, forexample, a random dimer, trimer, quatramer, pentamer, hexamer, septamer,octamer, nonamer, decamer, or higher multimer sequence of any length. Insome embodiments, the target binding region is the same for all barcodesattached to a given bead. In some embodiments, the target bindingregions for the plurality of barcodes attached to a given bead cancomprise two or more different target binding sequences. A targetbinding region can be, or be about, 5, 10, 15, 20, 25, 30, 35, 40, 45,50, or a number or a range between any two of these values, nucleotidesin length. A target binding region can be at most about 5, 10, 15, 20,25, 30, 35, 40, 45, 50 or more nucleotides in length.

In some embodiments, a target-binding region can comprise an oligo(dT)which can hybridize with mRNAs comprising polyadenylated ends. Atarget-binding region can be gene-specific. For example, atarget-binding region can be configured to hybridize to a specificregion of a target. A target-binding region can be, or be about, 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26 27, 28, 29, 30, or a number or a range between any two ofthese values, nucleotides in length. A target-binding region can be atleast, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 27, 28, 29, or 30,nucleotides in length. A target-binding region can be about 5-30nucleotides in length. When a barcode comprises a gene-specifictarget-binding region, the barcode can be referred to herein as agene-specific barcode.

Orientation Property

A barcode can comprise one or more orientation properties which can beused to orient (e.g., align) the barcodes. A barcode can comprise amoiety for isoelectric focusing. Different barcodes can comprisedifferent isoelectric focusing points. When these barcodes areintroduced to a sample, the sample can undergo isoelectric focusing inorder to orient the barcodes into a known way. In this way, theorientation property can be used to develop a known map of barcodes in asample. Exemplary orientation properties can include, electrophoreticmobility (e.g., based on size of the barcode), isoelectric point, spin,conductivity, and/or self-assembly. For example, barcodes with anorientation property of self-assembly, can self-assemble into a specificorientation (e.g., nucleic acid nanostructure) upon activation.

Affinity Property

A barcode can comprise one or more affinity properties. For example, aspatial label can comprise an affinity property. An affinity propertycan include a chemical and/or biological moiety that can facilitatebinding of the barcode to another entity (e.g., cell receptor). Forexample, an affinity property can comprise an antibody, for example, anantibody specific for a specific moiety (e.g., receptor) on a sample. Insome embodiments, the antibody can guide the barcode to a specific celltype or molecule. Targets at and/or near the specific cell type ormolecule can be stochastically labeled. The affinity property can, insome embodiments, provide spatial information in addition to thenucleotide sequence of the spatial label because the antibody can guidethe barcode to a specific location. The antibody can be a therapeuticantibody, for example a monoclonal antibody or a polyclonal antibody.The antibody can be humanized or chimeric. The antibody can be a nakedantibody or a fusion antibody.

The antibody can be a full-length (i.e., naturally occurring or formedby normal immunoglobulin gene fragment recombinatorial processes)immunoglobulin molecule (e.g., an IgG antibody) or an immunologicallyactive (i.e., specifically binding) portion of an immunoglobulinmolecule, like an antibody fragment.

The antibody fragment can be, for example, a portion of an antibody suchas F(ab′)2, Fab′, Fab, Fv, sFv and the like. In some embodiments, theantibody fragment can bind with the same antigen that is recognized bythe full-length antibody. The antibody fragment can include isolatedfragments consisting of the variable regions of antibodies, such as the“Fv” fragments consisting of the variable regions of the heavy and lightchains and recombinant single chain polypeptide molecules in which lightand heavy variable regions are connected by a peptide linker (“scFvproteins”). Exemplary antibodies can include, but are not limited to,antibodies for cancer cells, antibodies for viruses, antibodies thatbind to cell surface receptors (CD8, CD34, CD45), and therapeuticantibodies.

Universal Adaptor Primer

A barcode can comprise one or more universal adaptor primers. Forexample, a gene-specific barcode, such as a gene-specific stochasticbarcode, can comprise a universal adaptor primer. A universal adaptorprimer can refer to a nucleotide sequence that is universal across allbarcodes. A universal adaptor primer can be used for buildinggene-specific barcodes. A universal adaptor primer can be, or be about,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26 27, 28, 29, 30, or a number or a range betweenany two of these nucleotides in length. A universal adaptor primer canbe at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 27, 28, 29, or 30nucleotides in length. A universal adaptor primer can be from 5-30nucleotides in length.

Linker

When a barcode comprises more than one of a type of label (e.g., morethan one cell label or more than one barcode sequence, such as onemolecular label), the labels may be interspersed with a linker labelsequence. A linker label sequence can be at least about 5, 10, 15, 20,25, 30, 35, 40, 45, 50 or more nucleotides in length. A linker labelsequence can be at most about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 ormore nucleotides in length. In some instances, a linker label sequenceis 12 nucleotides in length. A linker label sequence can be used tofacilitate the synthesis of the barcode. The linker label can comprisean error-correcting (e.g., Hamming) code.

Solid Supports

Barcodes, such as stochastic barcodes, disclosed herein can, in someembodiments, be associated with a solid support. The solid support canbe, for example, a synthetic particle. In some embodiments, some or allof the barcode sequence, such as molecular labels for stochasticbarcodes (e.g., the first barcode sequences) of a plurality of barcodes(e.g., the first plurality of barcodes) on a solid support differ by atleast one nucleotide. The cell labels of the barcodes on the same solidsupport can be the same. The cell labels of the barcodes on differentsolid supports can differ by at least one nucleotide. For example, firstcell labels of a first plurality of barcodes on a first solid supportcan have the same sequence, and second cell labels of a second pluralityof barcodes on a second solid support can have the same sequence. Thefirst cell labels of the first plurality of barcodes on the first solidsupport and the second cell labels of the second plurality of barcodeson the second solid support can differ by at least one nucleotide. Acell label can be, for example, about 5-20 nucleotides long. A barcodesequence can be, for example, about 5-20 nucleotides long. The syntheticparticle can be, for example, a bead.

The bead can be, for example, a silica gel bead, a controlled pore glassbead, a magnetic bead, a Dynabead, a Sephadex/Sepharose bead, acellulose bead, a polystyrene bead, or any combination thereof. The beadcan comprise a material such as polydimethylsiloxane (PDMS),polystyrene, glass, polypropylene, agarose, gelatin, hydrogel,paramagnetic, ceramic, plastic, glass, methylstyrene, acrylic polymer,titanium, latex, Sepharose, cellulose, nylon, silicone, or anycombination thereof.

In some embodiments, the bead can be a polymeric bead, for example adeformable bead or a gel bead, functionalized with barcodes orstochastic barcodes (such as gel beads from 10× Genomics (San Francisco,Calif.). In some implementation, a gel bead can comprise a polymer basedgels. Gel beads can be generated, for example, by encapsulating one ormore polymeric precursors into droplets. Upon exposure of the polymericprecursors to an accelerator (e.g., tetramethylethylenediamine (TEMED)),a gel bead may be generated.

In some embodiments, the particle can be degradable. For example, thepolymeric bead can dissolve, melt, or degrade, for example, under adesired condition. The desired condition can include an environmentalcondition. The desired condition may result in the polymeric beaddissolving, melting, or degrading in a controlled manner. A gel bead maydissolve, melt, or degrade due to a chemical stimulus, a physicalstimulus, a biological stimulus, a thermal stimulus, a magneticstimulus, an electric stimulus, a light stimulus, or any combinationsthereof.

Analytes and/or reagents, such as oligonucleotide barcodes, for example,may be coupled/immobilized to the interior surface of a gel bead (e.g.,the interior accessible via diffusion of an oligonucleotide barcodeand/or materials used to generate an oligonucleotide barcode) and/or theouter surface of a gel bead or any other microcapsule described herein.Coupling/immobilization may be via any form of chemical bonding (e.g.,covalent bond, ionic bond) or physical phenomena (e.g., Van der Waalsforces, dipole-dipole interactions, etc.). In some embodiments,coupling/immobilization of a reagent to a gel bead or any othermicrocapsule described herein may be reversible, such as, for example,via a labile moiety (e.g., via a chemical cross-linker, includingchemical cross-linkers described herein). Upon application of astimulus, the labile moiety may be cleaved and the immobilized reagentset free. In some embodiments, the labile moiety is a disulfide bond.For example, in the case where an oligonucleotide barcode is immobilizedto a gel bead via a disulfide bond, exposure of the disulfide bond to areducing agent can cleave the disulfide bond and free theoligonucleotide barcode from the bead. The labile moiety may be includedas part of a gel bead or microcapsule, as part of a chemical linker thatlinks a reagent or analyte to a gel bead or microcapsule, and/or as partof a reagent or analyte. In some embodiments, at least one barcode ofthe plurality of barcodes can be immobilized on the particle, partiallyimmobilized on the particle, enclosed in the particle, partiallyenclosed in the particle, or any combination thereof.

In some embodiments, a gel bead can comprise a wide range of differentpolymers including but not limited to: polymers, heat sensitivepolymers, photosensitive polymers, magnetic polymers, pH sensitivepolymers, salt-sensitive polymers, chemically sensitive polymers,polyelectrolytes, polysaccharides, peptides, proteins, and/or plastics.Polymers may include but are not limited to materials such aspoly(N-isopropylacrylamide) (PNIPAAm), poly(styrene sulfonate) (PSS),poly(allyl amine) (PAAm), poly(acrylic acid) (PAA), poly(ethylene imine)(PEI), poly(diallyldimethyl-ammonium chloride) (PDADMAC), poly(pyrolle)(PPy), poly(vinylpyrrolidone) (PVPON), poly(vinyl pyridine) (PVP),poly(methacrylic acid) (PMAA), poly(methyl methacrylate) (PMMA),polystyrene (PS), poly(tetrahydrofuran) (PTHF), poly(phthaladehyde)(PTHF), poly(hexyl viologen) (PHV), poly(L-lysine) (PLL),poly(L-arginine) (PARG), poly(lactic-co-glycolic acid) (PLGA).

Numerous chemical stimuli can be used to trigger the disruption,dissolution, or degradation of the beads. Examples of these chemicalchanges may include, but are not limited to pH-mediated changes to thebead wall, disintegration of the bead wall via chemical cleavage ofcrosslink bonds, triggered depolymerization of the bead wall, and beadwall switching reactions. Bulk changes may also be used to triggerdisruption of the beads.

Bulk or physical changes to the microcapsule through various stimulialso offer many advantages in designing capsules to release reagents.Bulk or physical changes occur on a macroscopic scale, in which beadrupture is the result of mechano-physical forces induced by a stimulus.These processes may include, but are not limited to pressure inducedrupture, bead wall melting, or changes in the porosity of the bead wall.

Biological stimuli may also be used to trigger disruption, dissolution,or degradation of beads. Generally, biological triggers resemblechemical triggers, but many examples use biomolecules, or moleculescommonly found in living systems such as enzymes, peptides, saccharides,fatty acids, nucleic acids and the like. For example, beads may comprisepolymers with peptide cross-links that are sensitive to cleavage byspecific proteases. More specifically, one example may comprise amicrocapsule comprising GFLGK peptide cross links. Upon addition of abiological trigger such as the protease Cathepsin B, the peptide crosslinks of the shell well are cleaved and the contents of the beads arereleased. In other cases, the proteases may be heat-activated. Inanother example, beads comprise a shell wall comprising cellulose.Addition of the hydrolytic enzyme chitosan serves as biologic triggerfor cleavage of cellulosic bonds, depolymerization of the shell wall,and release of its inner contents.

The beads may also be induced to release their contents upon theapplication of a thermal stimulus. A change in temperature can cause avariety changes to the beads. A change in heat may cause melting of abead such that the bead wall disintegrates. In other cases, the heat mayincrease the internal pressure of the inner components of the bead suchthat the bead ruptures or explodes. In still other cases, the heat maytransform the bead into a shrunken dehydrated state. The heat may alsoact upon heat-sensitive polymers within the wall of a bead to causedisruption of the bead.

Inclusion of magnetic nanoparticles to the bead wall of microcapsulesmay allow triggered rupture of the beads as well as guide the beads inan array. A device of this disclosure may comprise magnetic beads foreither purpose. In one example, incorporation of Fe₃O₄ nanoparticlesinto polyelectrolyte containing beads triggers rupture in the presenceof an oscillating magnetic field stimulus.

A bead may also be disrupted, dissolved, or degraded as the result ofelectrical stimulation. Similar to magnetic particles described in theprevious section, electrically sensitive beads can allow for bothtriggered rupture of the beads as well as other functions such asalignment in an electric field, electrical conductivity or redoxreactions. In one example, beads containing electrically sensitivematerial are aligned in an electric field such that release of innerreagents can be controlled. In other examples, electrical fields mayinduce redox reactions within the bead wall itself that may increaseporosity.

A light stimulus may also be used to disrupt the beads. Numerous lighttriggers are possible and may include systems that use various moleculessuch as nanoparticles and chromophores capable of absorbing photons ofspecific ranges of wavelengths. For example, metal oxide coatings can beused as capsule triggers. UV irradiation of polyelectrolyte capsulescoated with SiO₂ may result in disintegration of the bead wall. In yetanother example, photo switchable materials such as azobenzene groupsmay be incorporated in the bead wall. Upon the application of UV orvisible light, chemicals such as these undergo a reversible cis-to-transisomerization upon absorption of photons. In this aspect, incorporationof photon switches result in a bead wall that may disintegrate or becomemore porous upon the application of a light trigger.

For example, in a non-limiting example of barcoding (e.g., stochasticbarcoding) illustrated in FIG. 2 , after introducing cells such assingle cells onto a plurality of microwells of a microwell array atblock 208, beads can be introduced onto the plurality of microwells ofthe microwell array at block 212. Each microwell can comprise one bead.The beads can comprise a plurality of barcodes. A barcode can comprise a5′ amine region attached to a bead. The barcode can comprise a universallabel, a barcode sequence (e.g., a molecular label), a target-bindingregion, or any combination thereof.

The barcodes disclosed herein can be associated with (e.g., attached to)a solid support (e.g., a bead). The barcodes associated with a solidsupport can each comprise a barcode sequence selected from a groupcomprising at least 100 or 1000 barcode sequences with unique sequences.In some embodiments, different barcodes associated with a solid supportcan comprise barcode sequences of different sequences. In someembodiments, a percentage of barcodes associated with a solid supportcomprises the same cell label. For example, the percentage can be, or beabout 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or arange between any two of these values. As another example, thepercentage can be at least, or at most 60%, 70%, 80%, 85%, 90%, 95%,97%, 99%, or 100%. In some embodiments, barcodes associated with a solidsupport can have the same cell label. The barcodes associated withdifferent solid supports can have different cell labels selected from agroup comprising at least 100 or 1000 cell labels with unique sequences.

The barcodes disclosed herein can be associated to (e.g., attached to) asolid support (e.g., a bead). In some embodiments, stochasticallybarcoding the plurality of targets in the sample can be performed with asolid support including a plurality of synthetic particles associatedwith the plurality of barcodes. In some embodiments, the solid supportcan include a plurality of synthetic particles associated with theplurality of barcodes. The spatial labels of the plurality of barcodeson different solid supports can differ by at least one nucleotide. Thesolid support can, for example, include the plurality of barcodes in twodimensions or three dimensions. The synthetic particles can be beads.The beads can be silica gel beads, controlled pore glass beads, magneticbeads, Dynabeads, Sephadex/Sepharose beads, cellulose beads, polystyrenebeads, or any combination thereof. The solid support can include apolymer, a matrix, a hydrogel, a needle array device, an antibody, orany combination thereof. In some embodiments, the solid supports can befree floating. In some embodiments, the solid supports can be embeddedin a semi-solid or solid array. The barcodes may not be associated withsolid supports. The barcodes can be individual nucleotides. The barcodescan be associated with a substrate.

As used herein, the terms “tethered,” “attached,” and “immobilized” areused interchangeably, and can refer to covalent or non-covalent meansfor attaching barcodes to a solid support. Any of a variety of differentsolid supports can be used as solid supports for attachingpre-synthesized barcodes or for in situ solid-phase synthesis ofbarcodes.

In some embodiments, the solid support is a bead. The bead can compriseone or more types of solid, porous, or hollow sphere, ball, bearing,cylinder, or other similar configuration which a nucleic acid can beimmobilized (e.g., covalently or non-covalently). The bead can be, forexample, composed of plastic, ceramic, metal, polymeric material, or anycombination thereof. A bead can be, or comprise, a discrete particlethat is spherical (e.g., microspheres) or have a non-spherical orirregular shape, such as cubic, cuboid, pyramidal, cylindrical, conical,oblong, or disc-shaped, and the like. In some embodiments, a bead can benon-spherical in shape.

Beads can comprise a variety of materials including, but not limited to,paramagnetic materials (e.g. magnesium, molybdenum, lithium, andtantalum), superparamagnetic materials (e.g. ferrite (Fe₃O₄; magnetite)nanoparticles), ferromagnetic materials (e.g. iron, nickel, cobalt, somealloys thereof, and some rare earth metal compounds), ceramic, plastic,glass, polystyrene, silica, methylstyrene, acrylic polymers, titanium,latex, Sepharose, agarose, hydrogel, polymer, cellulose, nylon, or anycombination thereof.

In some embodiments, the bead (e.g., the bead to which the labels areattached) is a hydrogel bead. In some embodiments, the bead compriseshydrogel.

Some embodiments disclosed herein include one or more particles (forexample beads). Each of the particles can comprise a plurality ofoligonucleotides (e.g., barcodes). Each of the plurality ofoligonucleotides can comprise a barcode sequence (e.g., a molecularlabel), a cell label, and a target-binding region (e.g., an oligo(dT)sequence, a gene-specific sequence, a random multimer, or a combinationthereof). The cell label sequence of each of the plurality ofoligonucleotides can be the same. The cell label sequences ofoligonucleotides on different particles can be different such that theoligonucleotides on different particles can be identified. The number ofdifferent cell label sequences can be different in differentimplementations. In some embodiments, the number of cell label sequencescan be, or about 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000,40000, 50000, 60000, 70000, 80000, 90000, 100000, 10⁶, 10⁷, 10⁸, 10⁹, anumber or a range between any two of these values, or more. In someembodiments, the number of cell label sequences can be at least, or atmost 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000,4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000,60000, 70000, 80000, 90000, 100000, 10⁶, 10⁷, 10⁸, or 10⁹. In someembodiments, no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50,60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, ormore of the plurality of the particles include oligonucleotides with thesame cell sequence. In some embodiment, the plurality of particles thatinclude oligonucleotides with the same cell sequence can be at most0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4%,5%, 6%, 7%, 8%, 9%, 10% or more. In some embodiments, none of theplurality of the particles has the same cell label sequence.

The plurality of oligonucleotides on each particle can comprisedifferent barcode sequences (e.g., molecular labels). In someembodiments, the number of barcode sequences can be, or about 10, 100,200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000,6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000,80000, 90000, 100000, 10⁶, 10⁷, 10⁸, 10⁹, or a number or a range betweenany two of these values. In some embodiments, the number of barcodesequences can be at least, or at most 10, 100, 200, 300, 400, 500, 600,700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000,10⁶, 10⁷, 10⁸, or 10⁹. For example, at least 100 of the plurality ofoligonucleotides comprise different barcode sequences. As anotherexample, in a single particle, at least 100, 500, 1000, 5000, 10000,15000, 20000, 50000, a number or a range between any two of thesevalues, or more of the plurality of oligonucleotides comprise differentbarcode sequences. Some embodiments provide a plurality of the particlescomprising barcodes. In some embodiments, the ratio of an occurrence (ora copy or a number) of a target to be labeled and the different barcodesequences can be at least 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9,1:10, 1:11, 1:12, 1:13, 1:14, 1:15, 1:16, 1:17, 1:18, 1:19, 1:20, 1:30,1:40, 1:50, 1:60, 1:70, 1:80, 1:90, or more. In some embodiments, eachof the plurality of oligonucleotides further comprises a sample label, auniversal label, or both. The particle can be, for example, ananoparticle or microparticle.

The size of the beads can vary. For example, the diameter of the beadcan range from 0.1 micrometer to 50 micrometer. In some embodiments, thediameters of beads can be, or be about, 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 20, 30, 40, 50 micrometer, or a number or a range between anytwo of these values.

The diameters of the bead can be related to the diameter of the wells ofthe substrate. In some embodiments, the diameters of the bead can be, orbe about, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, or a numberor a range between any two of these values, longer or shorter than thediameter of the well. The diameter of the beads can be related to thediameter of a cell (e.g., a single cell entrapped by a well of thesubstrate). In some embodiments, the diameters of the bead can be atleast, or at most, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%longer or shorter than the diameter of the well. The diameter of thebeads can be related to the diameter of a cell (e.g., a single cellentrapped by a well of the substrate). In some embodiments, thediameters of the beads can be, or be about, 10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, or a number or a rangebetween any two of these values, longer or shorter than the diameter ofthe cell. In some embodiments, the diameters of the beads can be atleast, or at most, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%,150%, 200%, 250%, or 300% longer or shorter than the diameter of thecell.

A bead can be attached to and/or embedded in a substrate. A bead can beattached to and/or embedded in a gel, hydrogel, polymer and/or matrix.The spatial position of a bead within a substrate (e.g., gel, matrix,scaffold, or polymer) can be identified using the spatial label presenton the barcode on the bead which can serve as a location address.

Examples of beads can include, but are not limited to, streptavidinbeads, agarose beads, magnetic beads, Dynabeads®, MACS® microbeads,antibody conjugated beads (e.g., anti-immunoglobulin microbeads),protein A conjugated beads, protein G conjugated beads, protein A/Gconjugated beads, protein L conjugated beads, oligo(dT) conjugatedbeads, silica beads, silica-like beads, anti-biotin microbeads,anti-fluorochrome microbeads, and BcMag™ Carboxyl-Terminated MagneticBeads.

A bead can be associated with (e.g. impregnated with) quantum dots orfluorescent dyes to make it fluorescent in one fluorescence opticalchannel or multiple optical channels. A bead can be associated with ironoxide or chromium oxide to make it paramagnetic or ferromagnetic. Beadscan be identifiable. For example, a bead can be imaged using a camera. Abead can have a detectable code associated with the bead. For example, abead can comprise a barcode. A bead can change size, for example due toswelling in an organic or inorganic solution. A bead can be hydrophobic.A bead can be hydrophilic. A bead can be biocompatible.

A solid support (e.g., bead) can be visualized. The solid support cancomprise a visualizing tag (e.g., fluorescent dye). A solid support(e.g., bead) can be etched with an identifier (e.g., a number). Theidentifier can be visualized through imaging the beads.

A solid support can comprise an insoluble, semi-soluble, or insolublematerial. A solid support can be referred to as “functionalized” when itincludes a linker, a scaffold, a building block, or other reactivemoiety attached thereto, whereas a solid support may be“nonfunctionalized” when it lack such a reactive moiety attachedthereto. The solid support can be employed free in solution, such as ina microtiter well format; in a flow-through format, such as in a column;or in a dipstick.

The solid support can comprise a membrane, paper, plastic, coatedsurface, flat surface, glass, slide, chip, or any combination thereof. Asolid support can take the form of resins, gels, microspheres, or othergeometric configurations. A solid support can comprise silica chips,microparticles, nanoparticles, plates, arrays, capillaries, flatsupports such as glass fiber filters, glass surfaces, metal surfaces(steel, gold silver, aluminum, silicon and copper), glass supports,plastic supports, silicon supports, chips, filters, membranes, microwellplates, slides, plastic materials including multiwell plates ormembranes (e.g., formed of polyethylene, polypropylene, polyamide,polyvinylidenedifluoride), and/or wafers, combs, pins or needles (e.g.,arrays of pins suitable for combinatorial synthesis or analysis) orbeads in an array of pits or nanoliter wells of flat surfaces such aswafers (e.g., silicon wafers), wafers with pits with or without filterbottoms.

The solid support can comprise a polymer matrix (e.g., gel, hydrogel).The polymer matrix may be able to permeate intracellular space (e.g.,around organelles). The polymer matrix may able to be pumped throughoutthe circulatory system.

A solid support can be a biological molecule. For example a solidsupport can be a nucleic acid, a protein, an antibody, a histone, acellular compartment, a lipid, a carbohydrate, and the like. Solidsupports that are biological molecules can be amplified, translated,transcribed, degraded, and/or modified (e.g., pegylated, sumoylated,acetylated, methylated). A solid support that is a biological moleculecan provide spatial and time information in addition to the spatiallabel that is attached to the biological molecule. For example, abiological molecule can comprise a first confirmation when unmodified,but can change to a second confirmation when modified. The differentconformations can expose barcodes (e.g., stochastic barcodes) of thedisclosure to targets. For example, a biological molecule can comprisebarcodes that are inaccessible due to folding of the biologicalmolecule. Upon modification of the biological molecule (e.g.,acetylation), the biological molecule can change conformation to exposethe barcodes. The timing of the modification can provide another timedimension to the method of barcoding of the disclosure.

In some embodiments, the biological molecule comprising barcode reagentsof the disclosure can be located in the cytoplasm of a cell. Uponactivation, the biological molecule can move to the nucleus, whereuponbarcoding can take place. In this way, modification of the biologicalmolecule can encode additional space-time information for the targetsidentified by the barcodes.

Substrates and Microwell Array

As used herein, a substrate can refer to a type of solid support. Asubstrate can refer to a solid support that can comprise barcodes andstochastic barcodes of the disclosure. A substrate can, for example,comprise a plurality of microwells. For example, a substrate can be awell array comprising two or more microwells. In some embodiments, amicrowell can comprise a small reaction chamber of defined volume. Insome embodiments, a microwell can entrap one or more cells. In someembodiments, a microwell can entrap only one cell. In some embodiments,a microwell can entrap one or more solid supports. In some embodiments,a microwell can entrap only one solid support. In some embodiments, amicrowell entraps a single cell and a single solid support (e.g., bead).A microwell can comprise combinatorial barcode reagents of thedisclosure.

Methods of Barcoding

The disclosure provides for methods for estimating the number ofdistinct targets at distinct locations in a physical sample (e.g.,tissue, organ, tumor, cell). The methods can comprise placing thebarcodes (e.g., stochastic barcodes) in close proximity with the sample,lysing the sample, associating distinct targets with the barcodes,amplifying the targets and/or digitally counting the targets. The methodcan further comprise analyzing and/or visualizing the informationobtained from the spatial labels on the barcodes. In some embodiments, amethod comprises visualizing the plurality of targets in the sample.Mapping the plurality of targets onto the map of the sample can includegenerating a two dimensional map or a three dimensional map of thesample. The two dimensional map and the three dimensional map can begenerated prior to or after barcoding (e.g., stochastically barcoding)the plurality of targets in the sample. Visualizing the plurality oftargets in the sample can include mapping the plurality of targets ontoa map of the sample. Mapping the plurality of targets onto the map ofthe sample can include generating a two dimensional map or a threedimensional map of the sample. The two dimensional map and the threedimensional map can be generated prior to or after barcoding theplurality of targets in the sample. in some embodiments, the twodimensional map and the three dimensional map can be generated before orafter lysing the sample. Lysing the sample before or after generatingthe two dimensional map or the three dimensional map can include heatingthe sample, contacting the sample with a detergent, changing the pH ofthe sample, or any combination thereof.

In some embodiments, barcoding the plurality of targets compriseshybridizing a plurality of barcodes with a plurality of targets tocreate barcoded targets (e.g., stochastically barcoded targets).Barcoding the plurality of targets can comprise generating an indexedlibrary of the barcoded targets. Generating an indexed library of thebarcoded targets can be performed with a solid support comprising theplurality of barcodes (e.g., stochastic barcodes).

Contacting a Sample and a Barcode

The disclosure provides for methods for contacting a sample (e.g.,cells) to a substrate of the disclosure. A sample comprising, forexample, a cell, organ, or tissue thin section, can be contacted tobarcodes (e.g., stochastic barcodes). The cells can be contacted, forexample, by gravity flow wherein the cells can settle and create amonolayer. The sample can be a tissue thin section. The thin section canbe placed on the substrate. The sample can be one-dimensional (e.g.,forms a planar surface). The sample (e.g., cells) can be spread acrossthe substrate, for example, by growing/culturing the cells on thesubstrate.

When barcodes are in close proximity to targets, the targets canhybridize to the barcode. The barcodes can be contacted at anon-depletable ratio such that each distinct target can associate with adistinct barcode of the disclosure. To ensure efficient associationbetween the target and the barcode, the targets can be crosslinked tothe barcode.

Cell Lysis

Following the distribution of cells and barcodes, the cells can be lysedto liberate the target molecules. Cell lysis can be accomplished by anyof a variety of means, for example, by chemical or biochemical means, byosmotic shock, or by means of thermal lysis, mechanical lysis, oroptical lysis. Cells can be lysed by addition of a cell lysis buffercomprising a detergent (e.g. SDS, Li dodecyl sulfate, Triton X-100,Tween-20, or NP-40), an organic solvent (e.g. methanol or acetone), ordigestive enzymes (e.g. proteinase K, pepsin, or trypsin), or anycombination thereof. To increase the association of a target and abarcode, the rate of the diffusion of the target molecules can bealtered by for example, reducing the temperature and/or increasing theviscosity of the lysate.

In some embodiments, the sample can be lysed using a filter paper. Thefilter paper can be soaked with a lysis buffer on top of the filterpaper. The filter paper can be applied to the sample with pressure whichcan facilitate lysis of the sample and hybridization of the targets ofthe sample to the substrate.

In some embodiments, lysis can be performed by mechanical lysis, heatlysis, optical lysis, and/or chemical lysis. Chemical lysis can includethe use of digestive enzymes such as proteinase K, pepsin, and trypsin.Lysis can be performed by the addition of a lysis buffer to thesubstrate. A lysis buffer can comprise Tris HCl. A lysis buffer cancomprise at least about 0.01, 0.05, 0.1, 0.5, or 1 M or more Tris HCl. Alysis buffer can comprise at most about 0.01, 0.05, 0.1, 0.5, or 1 M ormore Tris HCL. A lysis buffer can comprise about 0.1 M Tris HCl. The pHof the lysis buffer can be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or10 or more. The pH of the lysis buffer can be at most about 1, 2, 3, 4,5, 6, 7, 8, 9, or 10 or more. In some embodiments, the pH of the lysisbuffer is about 7.5. The lysis buffer can comprise a salt (e.g., LiCl).The concentration of salt in the lysis buffer can be at least about 0.1,0.5, or 1 M or more. The concentration of salt in the lysis buffer canbe at most about 0.1, 0.5, or 1 M or more. In some embodiments, theconcentration of salt in the lysis buffer is about 0.5M. The lysisbuffer can comprise a detergent (e.g., SDS, Li dodecyl sulfate, tritonX, tween, NP-40). The concentration of the detergent in the lysis buffercan be at least about 0.0001%, 0.0005%, 0.001%, 0.005%, 0.01%, 0.05%,0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, or 7% or more. The concentration ofthe detergent in the lysis buffer can be at most about 0.0001%, 0.0005%,0.001%, 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, or 7%or more. In some embodiments, the concentration of the detergent in thelysis buffer is about 1% Li dodecyl sulfate. The time used in the methodfor lysis can be dependent on the amount of detergent used. In someembodiments, the more detergent used, the less time needed for lysis.The lysis buffer can comprise a chelating agent (e.g., EDTA, EGTA). Theconcentration of a chelating agent in the lysis buffer can be at leastabout 1, 5, 10, 15, 20, 25, or 30 mM or more. The concentration of achelating agent in the lysis buffer can be at most about 1, 5, 10, 15,20, 25, or 30 mM or more. In some embodiments, the concentration ofchelating agent in the lysis buffer is about 10 mM. The lysis buffer cancomprise a reducing reagent (e.g., beta-mercaptoethanol, DTT). Theconcentration of the reducing reagent in the lysis buffer can be atleast about 1, 5, 10, 15, or 20 mM or more. The concentration of thereducing reagent in the lysis buffer can be at most about 1, 5, 10, 15,or 20 mM or more. In some embodiments, the concentration of reducingreagent in the lysis buffer is about 5 mM. In some embodiments, a lysisbuffer can comprise about 0.1M TrisHCl, about pH 7.5, about 0.5M LiCl,about 1% lithium dodecyl sulfate, about 10 mM EDTA, and about 5 mM DTT.

Lysis can be performed at a temperature of about 4, 10, 15, 20, 25, or30° C. Lysis can be performed for about 1, 5, 10, 15, or 20 or moreminutes. A lysed cell can comprise at least about 100000, 200000,300000, 400000, 500000, 600000, or 700000 or more target nucleic acidmolecules. A lysed cell can comprise at most about 100000, 200000,300000, 400000, 500000, 600000, or 700000 or more target nucleic acidmolecules.

Attachment of Barcodes to Target Nucleic Acid Molecules

Following lysis of the cells and release of nucleic acid moleculestherefrom, the nucleic acid molecules can randomly associate with thebarcodes of the co-localized solid support. Association can comprisehybridization of a barcode's target recognition region to acomplementary portion of the target nucleic acid molecule (e.g.,oligo(dT) of the barcode can interact with a poly(A) tail of a target).The assay conditions used for hybridization (e.g. buffer pH, ionicstrength, temperature, etc.) can be chosen to promote formation ofspecific, stable hybrids. In some embodiments, the nucleic acidmolecules released from the lysed cells can associate with the pluralityof probes on the substrate (e.g., hybridize with the probes on thesubstrate). When the probes comprise oligo(dT), mRNA molecules canhybridize to the probes and be reverse transcribed. The oligo(dT)portion of the oligonucleotide can act as a primer for first strandsynthesis of the cDNA molecule. For example, in a non-limiting exampleof barcoding illustrated in FIG. 2 , at block 216, mRNA molecules canhybridize to barcodes on beads. For example, single-stranded nucleotidefragments can hybridize to the target-binding regions of barcodes.

Attachment can further comprise ligation of a barcode's targetrecognition region and a portion of the target nucleic acid molecule.For example, the target binding region can comprise a nucleic acidsequence that can be capable of specific hybridization to a restrictionsite overhang (e.g. an EcoRI sticky-end overhang). The assay procedurecan further comprise treating the target nucleic acids with arestriction enzyme (e.g. EcoRI) to create a restriction site overhang.The barcode can then be ligated to any nucleic acid molecule comprisinga sequence complementary to the restriction site overhang. A ligase(e.g., T4 DNA ligase) can be used to join the two fragments.

For example, in a non-limiting example of barcoding illustrated in FIG.2 , at block 220, the labeled targets from a plurality of cells (or aplurality of samples) (e.g., target-barcode molecules) can besubsequently pooled, for example, into a tube. The labeled targets canbe pooled by, for example, retrieving the barcodes and/or the beads towhich the target-barcode molecules are attached.

The retrieval of solid support-based collections of attachedtarget-barcode molecules can be implemented by use of magnetic beads andan externally-applied magnetic field. Once the target-barcode moleculeshave been pooled, all further processing can proceed in a singlereaction vessel. Further processing can include, for example, reversetranscription reactions, amplification reactions, cleavage reactions,dissociation reactions, and/or nucleic acid extension reactions. Furtherprocessing reactions can be performed within the microwells, that is,without first pooling the labeled target nucleic acid molecules from aplurality of cells.

Reverse Transcription

The disclosure provides for a method to create a target-barcodeconjugate using reverse transcription (e.g., at block 224 of FIG. 2 ).The target-barcode conjugate can comprise the barcode and acomplementary sequence of all or a portion of the target nucleic acid(i.e. a barcoded cDNA molecule, such as a stochastically barcoded cDNAmolecule). Reverse transcription of the associated RNA molecule canoccur by the addition of a reverse transcription primer along with thereverse transcriptase. The reverse transcription primer can be anoligo(dT) primer, a random hexanucleotide primer, or a target-specificoligonucleotide primer. Oligo(dT) primers can be, or can be about, 12-18nucleotides in length and bind to the endogenous poly(A) tail at the 3′end of mammalian mRNA. Random hexanucleotide primers can bind to mRNA ata variety of complementary sites. Target-specific oligonucleotideprimers typically selectively prime the mRNA of interest.

In some embodiments, reverse transcription of the labeled-RNA moleculecan occur by the addition of a reverse transcription primer. In someembodiments, the reverse transcription primer is an oligo(dT) primer,random hexanucleotide primer, or a target-specific oligonucleotideprimer. Generally, oligo(dT) primers are 12-18 nucleotides in length andbind to the endogenous poly(A)+ tail at the 3′ end of mammalian mRNA.Random hexanucleotide primers can bind to mRNA at a variety ofcomplementary sites. Target-specific oligonucleotide primers typicallyselectively prime the mRNA of interest.

Reverse transcription can occur repeatedly to produce multiplelabeled-cDNA molecules. The methods disclosed herein can compriseconducting at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, or 20 reverse transcription reactions. The methodcan comprise conducting at least about 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, or 100 reverse transcription reactions.

Amplification

One or more nucleic acid amplification reactions (e.g., at block 228 ofFIG. 2 ) can be performed to create multiple copies of the labeledtarget nucleic acid molecules. Amplification can be performed in amultiplexed manner, wherein multiple target nucleic acid sequences areamplified simultaneously. The amplification reaction can be used to addsequencing adaptors to the nucleic acid molecules. The amplificationreactions can comprise amplifying at least a portion of a sample label,if present. The amplification reactions can comprise amplifying at leasta portion of the cell label and/or barcode sequence (e.g., molecularlabel). The amplification reactions can comprise amplifying at least aportion of a sample tag, a cell label, a spatial label, a barcode (e.g.,a molecular label), a target nucleic acid, or a combination thereof. Theamplification reactions can comprise amplifying 0.5%, 1%, 2%, 3%, 4%,5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 100%, or a range or anumber between any two of these values, of the plurality of nucleicacids. The method can further comprise conducting one or more cDNAsynthesis reactions to produce one or more cDNA copies of target-barcodemolecules comprising a sample label, a cell label, a spatial label,and/or a barcode sequence (e.g., a molecular label).

In some embodiments, amplification can be performed using a polymerasechain reaction (PCR). As used herein, PCR can refer to a reaction forthe in vitro amplification of specific DNA sequences by the simultaneousprimer extension of complementary strands of DNA. As used herein, PCRcan encompass derivative forms of the reaction, including but notlimited to, RT-PCR, real-time PCR, nested PCR, quantitative PCR,multiplexed PCR, digital PCR, and assembly PCR.

Amplification of the labeled nucleic acids can comprise non-PCR basedmethods. Examples of non-PCR based methods include, but are not limitedto, multiple displacement amplification (MDA), transcription-mediatedamplification (TMA), nucleic acid sequence-based amplification (NASBA),strand displacement amplification (SDA), real-time SDA, rolling circleamplification, or circle-to-circle amplification. Other non-PCR-basedamplification methods include multiple cycles of DNA-dependent RNApolymerase-driven RNA transcription amplification or RNA-directed DNAsynthesis and transcription to amplify DNA or RNA targets, a ligasechain reaction (LCR), and a Qβ replicase (Qβ) method, use of palindromicprobes, strand displacement amplification, oligonucleotide-drivenamplification using a restriction endonuclease, an amplification methodin which a primer is hybridized to a nucleic acid sequence and theresulting duplex is cleaved prior to the extension reaction andamplification, strand displacement amplification using a nucleic acidpolymerase lacking 5′ exonuclease activity, rolling circleamplification, and ramification extension amplification (RAM). In someembodiments, the amplification does not produce circularizedtranscripts.

In some embodiments, the methods disclosed herein further compriseconducting a polymerase chain reaction on the labeled nucleic acid(e.g., labeled-RNA, labeled-DNA, labeled-cDNA) to produce alabeled-amplicon (e.g., a stochastically labeled-amplicon). Thelabeled-amplicon can be double-stranded molecule. The double-strandedmolecule can comprise a double-stranded RNA molecule, a double-strandedDNA molecule, or a RNA molecule hybridized to a DNA molecule. One orboth of the strands of the double-stranded molecule can comprise asample label, a spatial label, a cell label, and/or a barcode sequence(e.g., a molecular label). The labeled-amplicon can be a single-strandedmolecule. The single-stranded molecule can comprise DNA, RNA, or acombination thereof. The nucleic acids of the disclosure can comprisesynthetic or altered nucleic acids.

Amplification can comprise use of one or more non-natural nucleotides.Non-natural nucleotides can comprise photolabile or triggerablenucleotides. Examples of non-natural nucleotides can include, but arenot limited to, peptide nucleic acid (PNA), morpholino and lockednucleic acid (LNA), as well as glycol nucleic acid (GNA) and threosenucleic acid (TNA). Non-natural nucleotides can be added to one or morecycles of an amplification reaction. The addition of the non-naturalnucleotides can be used to identify products as specific cycles or timepoints in the amplification reaction.

Conducting the one or more amplification reactions can comprise the useof one or more primers. The one or more primers can comprise, forexample, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or morenucleotides. The one or more primers can comprise at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or more nucleotides. The one ormore primers can comprise less than 12-15 nucleotides. The one or moreprimers can anneal to at least a portion of the plurality of labeledtargets (e.g., stochastically labeled targets). The one or more primerscan anneal to the 3′ end or 5′ end of the plurality of labeled targets.The one or more primers can anneal to an internal region of theplurality of labeled targets. The internal region can be at least about50, 100, 150, 200, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310,320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450,460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590,600, 650, 700, 750, 800, 850, 900 or 1000 nucleotides from the 3′ endsthe plurality of labeled targets. The one or more primers can comprise afixed panel of primers. The one or more primers can comprise at leastone or more custom primers. The one or more primers can comprise atleast one or more control primers. The one or more primers can compriseat least one or more gene-specific primers.

The one or more primers can comprise a universal primer. The universalprimer can anneal to a universal primer binding site. The one or morecustom primers can anneal to a first sample label, a second samplelabel, a spatial label, a cell label, a barcode sequence (e.g., amolecular label), a target, or any combination thereof. The one or moreprimers can comprise a universal primer and a custom primer. The customprimer can be designed to amplify one or more targets. The targets cancomprise a subset of the total nucleic acids in one or more samples. Thetargets can comprise a subset of the total labeled targets in one ormore samples. The one or more primers can comprise at least 96 or morecustom primers. The one or more primers can comprise at least 960 ormore custom primers. The one or more primers can comprise at least 9600or more custom primers. The one or more custom primers can anneal to twoor more different labeled nucleic acids. The two or more differentlabeled nucleic acids can correspond to one or more genes.

Any amplification scheme can be used in the methods of the presentdisclosure. For example, in one scheme, the first round PCR can amplifymolecules attached to the bead using a gene specific primer and a primeragainst the universal Illumina sequencing primer 1 sequence. The secondround of PCR can amplify the first PCR products using a nested genespecific primer flanked by Illumina sequencing primer 2 sequence, and aprimer against the universal Illumina sequencing primer 1 sequence. Thethird round of PCR adds P5 and P7 and sample index to turn PCR productsinto an Illumina sequencing library. Sequencing using 150 bp×2sequencing can reveal the cell label and barcode sequence (e.g.,molecular label) on read 1, the gene on read 2, and the sample index onindex 1 read.

In some embodiments, nucleic acids can be removed from the substrateusing chemical cleavage. For example, a chemical group or a modifiedbase present in a nucleic acid can be used to facilitate its removalfrom a solid support. For example, an enzyme can be used to remove anucleic acid from a substrate. For example, a nucleic acid can beremoved from a substrate through a restriction endonuclease digestion.For example, treatment of a nucleic acid containing a dUTP or ddUTP withuracil-d-glycosylase (UDG) can be used to remove a nucleic acid from asubstrate. For example, a nucleic acid can be removed from a substrateusing an enzyme that performs nucleotide excision, such as a baseexcision repair enzyme, such as an apurinic/apyrimidinic (AP)endonuclease. In some embodiments, a nucleic acid can be removed from asubstrate using a photocleavable group and light. In some embodiments, acleavable linker can be used to remove a nucleic acid from thesubstrate. For example, the cleavable linker can comprise at least oneof biotin/avidin, biotin/streptavidin, biotin/neutravidin, Ig-protein A,a photo-labile linker, acid or base labile linker group, or an aptamer.

When the probes are gene-specific, the molecules can hybridize to theprobes and be reverse transcribed and/or amplified. In some embodiments,after the nucleic acid has been synthesized (e.g., reverse transcribed),it can be amplified. Amplification can be performed in a multiplexmanner, wherein multiple target nucleic acid sequences are amplifiedsimultaneously. Amplification can add sequencing adaptors to the nucleicacid.

In some embodiments, amplification can be performed on the substrate,for example, with bridge amplification. cDNAs can be homopolymer tailedin order to generate a compatible end for bridge amplification usingoligo(dT) probes on the substrate. In bridge amplification, the primerthat is complementary to the 3′ end of the template nucleic acid can bethe first primer of each pair that is covalently attached to the solidparticle. When a sample containing the template nucleic acid iscontacted with the particle and a single thermal cycle is performed, thetemplate molecule can be annealed to the first primer and the firstprimer is elongated in the forward direction by addition of nucleotidesto form a duplex molecule consisting of the template molecule and anewly formed DNA strand that is complementary to the template. In theheating step of the next cycle, the duplex molecule can be denatured,releasing the template molecule from the particle and leaving thecomplementary DNA strand attached to the particle through the firstprimer. In the annealing stage of the annealing and elongation step thatfollows, the complementary strand can hybridize to the second primer,which is complementary to a segment of the complementary strand at alocation removed from the first primer. This hybridization can cause thecomplementary strand to form a bridge between the first and secondprimers secured to the first primer by a covalent bond and to the secondprimer by hybridization. In the elongation stage, the second primer canbe elongated in the reverse direction by the addition of nucleotides inthe same reaction mixture, thereby converting the bridge to adouble-stranded bridge. The next cycle then begins, and thedouble-stranded bridge can be denatured to yield two single-strandednucleic acid molecules, each having one end attached to the particlesurface via the first and second primers, respectively, with the otherend of each unattached. In the annealing and elongation step of thissecond cycle, each strand can hybridize to a further complementaryprimer, previously unused, on the same particle, to form newsingle-strand bridges. The two previously unused primers that are nowhybridized elongate to convert the two new bridges to double-strandbridges.

The amplification reactions can comprise amplifying at least 1%, 2%, 3%,4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of theplurality of nucleic acids.

Amplification of the labeled nucleic acids can comprise PCR-basedmethods or non-PCR based methods. Amplification of the labeled nucleicacids can comprise exponential amplification of the labeled nucleicacids. Amplification of the labeled nucleic acids can comprise linearamplification of the labeled nucleic acids. Amplification can beperformed by polymerase chain reaction (PCR). PCR can refer to areaction for the in vitro amplification of specific DNA sequences by thesimultaneous primer extension of complementary strands of DNA. PCR canencompass derivative forms of the reaction, including but not limitedto, RT-PCR, real-time PCR, nested PCR, quantitative PCR, multiplexedPCR, digital PCR, suppression PCR, semi-suppressive PCR and assemblyPCR.

In some embodiments, amplification of the labeled nucleic acidscomprises non-PCR based methods. Examples of non-PCR based methodsinclude, but are not limited to, multiple displacement amplification(MDA), transcription-mediated amplification (TMA), nucleic acidsequence-based amplification (NASBA), strand displacement amplification(SDA), real-time SDA, rolling circle amplification, or circle-to-circleamplification. Other non-PCR-based amplification methods includemultiple cycles of DNA-dependent RNA polymerase-driven RNA transcriptionamplification or RNA-directed DNA synthesis and transcription to amplifyDNA or RNA targets, a ligase chain reaction (LCR), a Qβ replicase (Qβ),use of palindromic probes, strand displacement amplification,oligonucleotide-driven amplification using a restriction endonuclease,an amplification method in which a primer is hybridized to a nucleicacid sequence and the resulting duplex is cleaved prior to the extensionreaction and amplification, strand displacement amplification using anucleic acid polymerase lacking 5′ exonuclease activity, rolling circleamplification, and/or ramification extension amplification (RAM).

In some embodiments, the methods disclosed herein further compriseconducting a nested polymerase chain reaction on the amplified amplicon(e.g., target). The amplicon can be double-stranded molecule. Thedouble-stranded molecule can comprise a double-stranded RNA molecule, adouble-stranded DNA molecule, or a RNA molecule hybridized to a DNAmolecule. One or both of the strands of the double-stranded molecule cancomprise a sample tag or molecular identifier label. Alternatively, theamplicon can be a single-stranded molecule. The single-stranded moleculecan comprise DNA, RNA, or a combination thereof. The nucleic acids ofthe present invention can comprise synthetic or altered nucleic acids.

In some embodiments, the method comprises repeatedly amplifying thelabeled nucleic acid to produce multiple amplicons. The methodsdisclosed herein can comprise conducting at least about 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 amplificationreactions. Alternatively, the method comprises conducting at least about25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100amplification reactions.

Amplification can further comprise adding one or more control nucleicacids to one or more samples comprising a plurality of nucleic acids.Amplification can further comprise adding one or more control nucleicacids to a plurality of nucleic acids. The control nucleic acids cancomprise a control label.

Amplification can comprise use of one or more non-natural nucleotides.Non-natural nucleotides can comprise photolabile and/or triggerablenucleotides. Examples of non-natural nucleotides include, but are notlimited to, peptide nucleic acid (PNA), morpholino and locked nucleicacid (LNA), as well as glycol nucleic acid (GNA) and threose nucleicacid (TNA). Non-natural nucleotides can be added to one or more cyclesof an amplification reaction. The addition of the non-naturalnucleotides can be used to identify products as specific cycles or timepoints in the amplification reaction.

Conducting the one or more amplification reactions can comprise the useof one or more primers. The one or more primers can comprise one or moreoligonucleotides. The one or more oligonucleotides can comprise at leastabout 7-9 nucleotides. The one or more oligonucleotides can compriseless than 12-15 nucleotides. The one or more primers can anneal to atleast a portion of the plurality of labeled nucleic acids. The one ormore primers can anneal to the 3′ end and/or 5′ end of the plurality oflabeled nucleic acids. The one or more primers can anneal to an internalregion of the plurality of labeled nucleic acids. The internal regioncan be at least about 50, 100, 150, 200, 220, 230, 240, 250, 260, 270,280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410,420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550,560, 570, 580, 590, 600, 650, 700, 750, 800, 850, 900 or 1000nucleotides from the 3′ ends the plurality of labeled nucleic acids. Theone or more primers can comprise a fixed panel of primers. The one ormore primers can comprise at least one or more custom primers. The oneor more primers can comprise at least one or more control primers. Theone or more primers can comprise at least one or more housekeeping geneprimers. The one or more primers can comprise a universal primer. Theuniversal primer can anneal to a universal primer binding site. The oneor more custom primers can anneal to the first sample tag, the secondsample tag, the molecular identifier label, the nucleic acid or aproduct thereof. The one or more primers can comprise a universal primerand a custom primer. The custom primer can be designed to amplify one ormore target nucleic acids. The target nucleic acids can comprise asubset of the total nucleic acids in one or more samples. In someembodiments, the primers are the probes attached to the array of thedisclosure.

In some embodiments, barcoding (e.g., stochastically barcoding) theplurality of targets in the sample further comprises generating anindexed library of the barcoded fragments. The barcodes sequences ofdifferent barcodes (e.g., the molecular labels of different stochasticbarcodes) can be different from one another. Generating an indexedlibrary of the barcoded targets (e.g., stochastically barcoded targets)includes generating a plurality of indexed polynucleotides from theplurality of targets in the sample. For example, for an indexed libraryof the barcoded targets comprising a first indexed target and a secondindexed target, the label region of the first indexed polynucleotide candiffer from the label region of the second indexed polynucleotide by, byabout, by at least, or by at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20,30, 40, 50, or a number or a range between any two of these values,nucleotides. In some embodiments, generating an indexed library of thebarcoded targets includes contacting a plurality of targets, for examplemRNA molecules, with a plurality of oligonucleotides including a poly(T)region and a label region; and conducting a first strand synthesis usinga reverse transcriptase to produce single-strand labeled cDNA moleculeseach comprising a cDNA region and a label region, wherein the pluralityof targets includes at least two mRNA molecules of different sequencesand the plurality of oligonucleotides includes at least twooligonucleotides of different sequences. Generating an indexed libraryof the barcoded targets can further comprise amplifying thesingle-strand labeled cDNA molecules to produce double-strand labeledcDNA molecules; and conducting nested PCR on the double-strand labeledcDNA molecules to produce labeled amplicons. In some embodiments, themethod can include generating an adaptor-labeled amplicon.

Stochastic barcoding can use nucleic acid barcodes or tags to labelindividual nucleic acid (e.g., DNA or RNA) molecules. In someembodiments, it involves adding DNA barcodes or tags to cDNA moleculesas they are generated from mRNA. Nested PCR can be performed to minimizePCR amplification bias. Adaptors can be added for sequencing using, forexample, next generation sequencing (NGS). The sequencing results can beused to determine cell labels, barcode sequences (e.g., molecularlabels), and sequences of nucleotide fragments of the one or more copiesof the targets, for example at block 232 of FIG. 2 .

FIG. 3 is a schematic illustration showing a non-limiting exemplaryprocess of generating an indexed library of the barcoded targets (e.g.,stochastically barcoded targets), for example mRNAs. As shown in step 1,the reverse transcription process can encode each mRNA molecule with aunique barcode sequence (e.g., molecular label), a cell label, and auniversal PCR site. For example, RNA molecules 302 can be reversetranscribed to produce labeled cDNA molecules 304, including a cDNAregion 306, by the hybridization (e.g., stochastic hybridization) of aset of barcodes (e.g., stochastic barcodes) 310) to the poly(A) tailregion 308 of the RNA molecules 302. Each of the barcodes 310 cancomprise a target-binding region, for example a poly(dT) region 312, abarcode sequence or a molecular label 314, and a universal PCR region316.

In some embodiments, the cell label can include 3 to 20 nucleotides. Insome embodiments, the barcode sequence (e.g., molecular label) caninclude 3 to 20 nucleotides. In some embodiments, each of the pluralityof stochastic barcodes further comprises one or more of a universallabel and a cell label, wherein universal labels are the same for theplurality of stochastic barcodes on the solid support and cell labelsare the same for the plurality of stochastic barcodes on the solidsupport. In some embodiments, the universal label can include 3 to 20nucleotides. In some embodiments, the cell label comprises 3 to 20nucleotides.

In some embodiments, the label region 314 can include a barcode sequenceor a molecular label 318 and a cell label 320. In some embodiments, thelabel region 314 can include one or more of a universal label, adimension label, and a cell label. The barcode sequence or molecularlabel 318 can be, can be about, can be at least, or can be at most, 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or anumber or a range between any of these values, of nucleotides in length.The cell label 320 can be, can be about, can be at least, or can be atmost, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90,100, or a number or a range between any of these values, of nucleotidesin length. The universal label can be, can be about, can be at least, orcan be at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70,80, 90, 100, or a number or a range between any of these values, ofnucleotides in length. Universal labels can be the same for theplurality of stochastic barcodes on the solid support and cell labelsare the same for the plurality of stochastic barcodes on the solidsupport. The dimension label can be, can be about, can be at least, orcan be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70,80, 90, 100, or a number or a range between any of these values, ofnucleotides in length.

In some embodiments, the label region 314 can comprise, comprise about,comprise at least, or comprise at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800,900, 1000, or a number or a range between any of these values, differentlabels, such as a barcode sequence or a molecular label 318 and a celllabel 320. Each label can be, can be about, can be at least, or can beat most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90,100, or a number or a range between any of these values, of nucleotidesin length. A set of barcodes or stochastic barcodes 310 can contain,contain about, contain at least, or can be at most, 10, 20, 40, 50, 70,80, 90, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³,10¹⁴, 10¹⁵, 10²⁰, or a number or a range between any of these values,barcodes or stochastic barcodes 310. And the set of barcodes orstochastic barcodes 310 can, for example, each contain a unique labelregion 314. The labeled cDNA molecules 304 can be purified to removeexcess barcodes or stochastic barcodes 310. Purification can compriseAmpure bead purification.

As shown in step 2, products from the reverse transcription process instep 1 can be pooled into 1 tube and PCR amplified with a 1^(st) PCRprimer pool and a 1^(st) universal PCR primer. Pooling is possiblebecause of the unique label region 314. In particular, the labeled cDNAmolecules 304 can be amplified to produce nested PCR labeled amplicons322. Amplification can comprise multiplex PCR amplification.Amplification can comprise a multiplex PCR amplification with 96multiplex primers in a single reaction volume. In some embodiments,multiplex PCR amplification can utilize, utilize about, utilize atleast, or utilize at most, 10, 20, 40, 50, 70, 80, 90, 10², 10³, 10⁴,10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹°, 10″, 10¹², 10¹³, 10¹⁴, 10¹⁵, 10²⁰, or anumber or a range between any of these values, multiplex primers in asingle reaction volume. Amplification can comprise 1^(st) PCR primerpool 324 of custom primers 326A-C targeting specific genes and auniversal primer 328. The custom primers 326 can hybridize to a regionwithin the cDNA portion 306′ of the labeled cDNA molecule 304. Theuniversal primer 328 can hybridize to the universal PCR region 316 ofthe labeled cDNA molecule 304.

As shown in step 3 of FIG. 3 , products from PCR amplification in step 2can be amplified with a nested PCR primers pool and a 2^(nd) universalPCR primer. Nested PCR can minimize PCR amplification bias. For example,the nested PCR labeled amplicons 322 can be further amplified by nestedPCR. The nested PCR can comprise multiplex PCR with nested PCR primerspool 330 of nested PCR primers 332 a-c and a 2^(nd) universal PCR primer328′ in a single reaction volume. The nested PCR primer pool 328 cancontain, contain about, contain at least, or contain at most, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300,400, 500, 600, 700, 800, 900, 1000, or a number or a range between anyof these values, different nested PCR primers 330. The nested PCRprimers 332 can contain an adaptor 334 and hybridize to a region withinthe cDNA portion 306″ of the labeled amplicon 322. The universal primer328′ can contain an adaptor 336 and hybridize to the universal PCRregion 316 of the labeled amplicon 322. Thus, step 3 producesadaptor-labeled amplicon 338. In some embodiments, nested PCR primers332 and the 2^(nd) universal PCR primer 328′ may not contain theadaptors 334 and 336. The adaptors 334 and 336 can instead be ligated tothe products of nested PCR to produce adaptor-labeled amplicon 338.

As shown in step 4, PCR products from step 3 can be PCR amplified forsequencing using library amplification primers. In particular, theadaptors 334 and 336 can be used to conduct one or more additionalassays on the adaptor-labeled amplicon 338. The adaptors 334 and 336 canbe hybridized to primers 340 and 342. The one or more primers 340 and342 can be PCR amplification primers. The one or more primers 340 and342 can be sequencing primers. The one or more adaptors 334 and 336 canbe used for further amplification of the adaptor-labeled amplicons 338.The one or more adaptors 334 and 336 can be used for sequencing theadaptor-labeled amplicon 338. The primer 342 can contain a plate index344 so that amplicons generated using the same set of barcodes orstochastic barcodes 310 can be sequenced in one sequencing reactionusing next generation sequencing (NGS).

Errors in Cell Label Identification

Barcoding, such as stochastic barcoding, for example the Rhapsody™ assay(Cellular Research, Inc. (Palo Alto, Calif.)), can be based on beads.Molecules or targets such as mRNAs from different cells can hybridize tobarcodes (e.g., stochastic barcodes) on different beads. Barcodes ondifferent beads can have different cell labels, and barcodes on the samebeads can have the cell labels. For example, a single cell and a singlebead can be added to a microwell of a microwell plate such that prior toone bead is paired with one cell. Thus, cell labels are the same for alloligonucleotides on a bead, but differ between different beads, so thatall molecules from one cell can be identified with the same cell labelin the sequencing data. In some embodiments, the raw sequencing datafrom barcoding (e.g., stochastic barcoding) can include a higher numberof cell labels than the number of cell input of the experiment. Forexample, some molecules of 1,000 cells can be barcoded (e.g.,stochastically barcoded); however the raw sequencing data may indicate20000-200000 cell labels.

The sources of the higher number of cell labels may be different indifferent implementations. Without being bound by any particular theory,it is believed that in some embodiments, cells paired with no bead canbe lysed, and their nucleic acid contents can diffuse and associate withbeads not paired with any cells to result in false cell label signals.In some embodiments, during the manufacturing process of the beads, thecell labels may have a mutation in them which converts one cell labelinto another cell label. In this case, molecules from the same cell canappear to be from two different cells (e.g., as if they were from twodifferent beads because the cell label has mutated). Furthermore,substitution errors and non-substitution errors in the cell labels canoccur during PCR amplification prior to sequencing. In some embodiments,exonuclease treatment (e.g., at steps 216 in FIG. 2 ), may not beefficient such that single stranded DNA on the bead can hybridize andform PCR chimeras during the PCR process.

If uncorrected, the excess numbers of cell labels in raw sequencing datacan result in overestimated cell counts. Methods disclosed herein canseparate or distinguish signal cell labels (also referred to as truecell labels) from noise cell labels.

Identifying a Cell Label as a Signal Cell Label or a Noise Cell LabelBased on Second Derivatives

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) stochastically barcoding aplurality of targets in a sample of cells using a plurality ofstochastic barcodes to create a plurality of stochastically barcodedtargets, wherein each of the plurality of stochastic barcodes comprisesa cell label and a molecular label; (b) obtaining sequencing data of theplurality of stochastically barcoded targets; (c) determining the numberof molecular labels with distinct sequences associated with each of thecell labels of the plurality of stochastic barcodes; (d) determining arank of each of the cell labels of the plurality of stochastic barcodesbased on the number of molecular labels with distinct sequencesassociated with each of the cell labels; (e) generating a cumulative sumplot based on the number of molecular labels with distinct sequencesassociated with each of the cell labels determined in (c) and the rankof each of the cell labels determined in (d); (f) generating a secondderivative plot of the cumulative sum plot; (g) determining a minimum ofthe second derivative plot of the cumulative sum plot, wherein theminimum of the second derivative plot corresponds to a cell labelthreshold; and (h) identifying each of the cell labels as a signal celllabel (associated with a cell) or a noise cell label (not associatedwith cells) based on the number of molecular labels with distinctsequences associated with each of the cell labels determined in (c) andthe cell label threshold determined in (g).

The cause of a noise cell label can be different in differentimplementations. In some embodiments, a noise cell label can arise fromone or more PCR or sequencing errors. In some embodiments, a noise celllabel can arise from RNA molecules being released from dead cells. Insome embodiments, a noise cell label can arise from RNA molecules thatare released from cells not associated with beads attaching to beads notassociated with cells.

In some embodiments, the method comprises: (a) obtaining sequencing dataof a plurality of barcoded targets (e.g., stochastically barcodedtargets), wherein the plurality of barcoded targets is created from aplurality of targets in a sample of cells that are barcoded (e.g.,stochastically barcoded) using a plurality of barcodes (e.g., stochasticbarcodes), and wherein each of the plurality of barcodes comprises acell label and a molecular label; (b) determining a rank of each of thecell labels of the plurality of barcoded targets (or barcodes) based onthe number of molecular labels with distinct sequences associated witheach of the cell labels of the plurality of barcoded targets (orbarcodes); (c) determining a cell label threshold based on the number ofmolecular labels with distinct sequences associated with each of thecell labels and the rank of each of the cell labels of the plurality ofbarcoded targets (or barcodes) determined in (b); and identifying eachof the cell labels as a signal cell label or a noise cell label based onthe number of molecular labels with distinct sequences associated witheach of the cell labels and the cell label threshold determined in (c).

FIG. 4 is a flowchart showing a non-limiting exemplary method 400 ofidentifying a cell as a signal cell label or a noise cell label. Atblock 404, the method 400 can optionally barcode (e.g., stochasticallybarcode) targets in cells using barcodes (e.g., stochastic barcodes) tocreate barcoded targets (e.g., stochastically barcoded targets) asdescribed with reference to FIGS. 2-3 . Each of the barcodes cancomprise a cell label and a molecular label. Barcoded targets createdfrom targets of different cells of the plurality of cells can havedifferent cell labels. Barcoded targets created from targets of the samecell of the plurality of cells can have different molecular labels.

At block 408, the method 400 can obtain sequencing data of the barcodedtargets (e.g., stochastically barcoded targets) as described herein inthe section titled Sequencing. At block 412, the method 400 canoptionally determine the number of molecular labels with distinctsequences associated with each of the cell labels of the barcodes (orbarcoded targets). Determining the number of molecular labels withdistinct sequences associated with each of the cell labels of thebarcodes (or barcoded targets) can comprise: (1) counting the number ofmolecular labels with distinct sequences associated with the target inthe sequencing data; and (2) estimating the number of the target basedon the number of molecular labels with distinct sequences associatedwith the target in the sequencing data counted in (1). In someembodiments, the sequencing data obtained at block 408 includes thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the barcodes (or barcoded targets).

In some embodiments, the method can comprise removing sequencinginformation associated with molecular labels with distinct sequencesassociated with a target of the plurality of targets from the sequencingdata obtained in at block 408 if the number of the molecular labels withdistinct sequences associated with the target of the plurality oftargets is above or below a molecular label occurrence threshold. Themolecular label occurrence threshold can be different in differentimplementations. In some embodiments, the molecular label occurrencethreshold can be, or be about, 1000, 2000, 3000, 4000, 5000, 6000, 7000,8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000,90000, 100000, or a number or a range between any two of these values.In some embodiments, the molecular label occurrence threshold can be atleast, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or100000. In some embodiments, the molecular label occurrence thresholdcan be, or be about, 1%, 2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 20%, 30%, 40%,50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or anumber or a range between any two of these values. In some embodiments,the molecular label occurrence threshold can be at least, or at most,10%, 20%, 30%, 40%, 50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%.

At block 416, the method 400 can determine a rank of each of the celllabels of the barcodes (or barcoded targets). The rank of each of thecell labels of the barcodes (or barcoded targets) can be based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the plurality of barcodes (or barcoded targets).

At block 420, the method 400 can determine a cell label thresholdassociated with each of the cell labels of the plurality of barcodes (orbarcoded targets) and the rank of each of the cell labels of theplurality of barcodes (or barcoded targets) determined at block 416. Insome embodiments, determining the cell label threshold based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the plurality of barcodes (or barcoded targets)comprises: determining the cell label with the largest change in acumulative sum for the cell label with a rank n and a cumulative sum forthe cell label with the next rank n+1, wherein a number of molecularlabels with distinct sequences associated with the cell labelcorresponds to the cell label threshold.

In some embodiments, determining the cell label threshold based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the plurality of barcodes (or barcoded targets)and the rank of each of the cell labels of the plurality of barcodes (orbarcoded targets) determined at block 416 comprises: determining acumulative sum for each rank of the cell labels, wherein the cumulativesum for the rank comprises a summation of a number of molecular labelswith distinct sequences associated with each of the cell labels with alower rank; and determining a rank n of the cell labels with the largestchange in a cumulative sum for the rank n and a cumulative sum for thenext rank n+1, wherein the rank n of the cell labels with the largestchange in the cumulative sum and the cumulative sum for the next rankn+1 corresponds to the cell label threshold.

In some embodiments, determining the cell label threshold can comprise:generating a cumulative sum plot based on the number of molecular labelswith distinct sequences associated with each of the cell labels and therank of each of the cell labels determined in 416. Determining the celllabel threshold can further comprise: generating a second derivativeplot of the cumulative sum plot and determining a minimum of the secondderivative plot of the cumulative sum plot. The minimum of the secondderivative plot can correspond to the cell label threshold.

In some embodiments, generating the cumulative sum plot based on thenumber of molecular labels with distinct sequences associated with eachof the cell labels and the rank of each of the cell labels determined atblock 416 can comprise: determining a cumulative sum for each rank ofthe cell labels, wherein the cumulative sum for the rank comprises asummation of a number of molecular labels with distinct sequencesassociated with each of the cell labels with a lower rank. Generatingthe second derivative plot of the cumulative sum plot can comprisedetermining a difference between a cumulative sum of a first rank of thecell labels and a cumulative sum of a second rank of the cell labelsover a difference between the first rank and the second rank. In someembodiments, the difference between the first rank and the second rankis one. The cumulative sum plot can be a log-log plot. The log-log plotcan be a log 10-log 10 plot.

In some embodiments, the minimum is a global minimum. Determining theminimum of the second derivative plot can comprise determining a minimumof the second derivative plot above a threshold of a minimum number ofmolecular labels associated with each of the cell labels. The thresholdof the minimum number of molecular labels associated with each of thecell labels can be a percentile threshold. The threshold of the minimumnumber of molecular labels associated with each of the cell labels canbe determined based on the number of cells in the sample of cells. Forexample, the threshold of the minimum number of molecular labelsassociated with each of the cell labels can be greater if the number ofcells in the sample of cells is greater.

The threshold of the minimum number of molecular labels associated witheach of the cell labels can be different in different implementations.In some embodiments, the threshold of the minimum number of molecularlabels associated with each of the cell labels can be, or be about,1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000,30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or a number ora range between any two of these values. In some embodiments, thethreshold of the minimum number of molecular labels associated with eachof the cell labels can be at least, or at most, 1000, 2000, 3000, 4000,5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000,70000, 80000, 90000, or 100000. In some embodiments, the threshold ofthe minimum number of molecular labels associated with each of the celllabels can be, or be about, 1%, 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%,90%, or a number or a range between any two of these values. In someembodiments, the molecular label occurrence threshold can be at least,or at most, 1%, 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%, or 90%.

In some embodiments, determining the minimum of the second derivativeplot comprises determining a minimum of the second derivative plot belowa threshold of a maximum number of molecular labels associated with eachof the cell labels. The threshold of the maximum number of molecularlabels associated with each of the cell labels can be a percentilethreshold. The threshold of the maximum number of molecular labelsassociated with each of the cell labels can be determined based on thenumber of cells in the sample of cells. For example, the threshold ofthe maximum number of molecular labels associated with each of the celllabels can be greater if the number of cells in the sample of cells isgreater.

The threshold of the maximum number of molecular labels associated witheach of the cell labels can be different in different implementations.In some embodiments, the threshold of the maximum number of molecularlabels associated with each of the cell labels can be, or be about,1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000,30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or a number ora range between any two of these values. In some embodiments, thethreshold of the maximum number of molecular labels associated with eachof the cell labels can be at least, or at most, 1000, 2000, 3000, 4000,5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000,70000, 80000, 90000, or 100000. In some embodiments, the threshold ofthe maximum number of molecular labels associated with each of the celllabels can be, or be about, 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or a number or a rangebetween any two of these values. In some embodiments, the molecularlabel occurrence threshold can be at least, or at most, 10%, 20%, 30%,40%, 45%, 50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or99%.

At block 432, the method 400 can identify the cell label as a signalcell label or a noise cell label based on the number of molecular labelswith distinct sequences associated with the cell label and the celllabel threshold. Each of the cell labels is identified as the signalcell label if the number of molecular labels with distinct sequencesassociated with the each of the cell labels determined in (c) is greaterthan the cell label threshold. Each of the cell labels can be identifiedas a noise cell label if the number of molecular labels with distinctsequences associated with the each of the cell labels determined in (c)is not greater than the cell label threshold. In some embodiments, themethod comprises removing, if a cell label of the plurality of barcodes(or barcoded targets) is identified as a noise cell label in 432,sequencing information associated with the identified cell label fromthe sequencing data obtained at block 408.

Identifying a Cell Label as a Signal Cell Label or a Noise Cell LabelBased on Clustering

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) barcoding (e.g.,stochastically barcoding) a plurality of targets in a sample of cellsusing a plurality of barcodes (e.g., stochastic barcodes) to create aplurality of barcoded targets (e.g., stochastically barcoded targets),wherein each of the plurality of barcodes comprises a cell label and amolecular label, wherein barcoded targets created from targets ofdifferent cells of the plurality of cells have different cell labels,and wherein barcoded targets created from targets of the same cell ofthe plurality of cells have different molecular labels; (b) obtainingsequencing data of the plurality of barcoded targets; (c) determining afeature vector of each cell label of the plurality of barcodes (orbarcoded targets), wherein the feature vector comprise numbers ofmolecular labels with distinct sequences associated with the each celllabel; (d) determining a cluster for the each cell label of theplurality of barcodes (or barcoded targets) based on the feature vector;and (e) identifying the each cell label of the plurality of barcodes (orbarcoded targets) as a signal cell label or a noise cell label based ona number of cell labels in the cluster and a cluster size threshold.

Disclosed herein are methods for identifying a signal cell label. Insome embodiments, the method comprises: (a) obtaining sequencing data ofa plurality of barcoded targets (e.g., stochastically barcoded targets),wherein the plurality of barcoded targets (e.g., stochastically barcodedtargets) is create from a plurality of targets in a sample of cells thatare barcoded (e.g., stochastically barcoded) using a plurality ofbarcodes (e.g., stochastic barcodes), wherein each of the plurality ofbarcodes comprises a cell label and a molecular label, wherein barcodedtargets created from targets of different cells of the plurality ofcells have different cell labels, and wherein barcoded targets createdfrom targets of the same cell of the plurality of cells have differentmolecular labels; (b) determining a feature vector of each cell label ofthe plurality of barcoded targets, wherein the feature vector comprisenumbers of molecular labels with distinct sequences associated with theeach cell label; (c) determining a cluster for the each cell label ofthe plurality of barcoded targets based on the feature vector; and (d)identifying the each cell label of the plurality of barcoded targets asa signal cell label or a noise cell label based on a number of celllabels in the cluster and a cluster size threshold.

FIG. 5 is a flowchart showing another non-limiting exemplary method ofidentifying a cell as a signal cell label or a noise cell label. Atblock 504, the method 500 can optionally barcode (e.g., stochasticallybarcode) targets in cells using stochastic barcodes to create barcodedtargets (e.g., stochastically barcoded targets) as described withreference to FIGS. 2-3 . Each of the barcodes comprises a cell label anda molecular label. Barcoded targets created from targets of differentcells of the plurality of cells can have different cell labels. Barcodedtargets created from targets of the same cell of the plurality of cellscan have different molecular labels

At block 508, the method 500 can obtain sequencing data of the barcodedtargets. At block 508, the method 500 can optionally determine thenumber of molecular labels with distinct sequences associated with eachof the cell labels of the barcodes (or barcoded targets). Determiningthe number of molecular labels with distinct sequences associated witheach of the cell labels of the barcodes (or barcoded targets) cancomprise: (1) counting the number of molecular labels with distinctsequences associated with the target in the sequencing data; and (2)estimating the number of the target based on the number of molecularlabels with distinct sequences associated with the target in thesequencing data counted in (1). In some embodiments, the sequencing dataobtained at block 508 includes the number of molecular labels withdistinct sequences associated with each of the cell labels of thebarcodes (or barcoded targets).

At block 512, the method 500 can determine a feature vector of the celllabel. The feature vector can comprise the numbers of the molecularlabels with distinct sequences associated with the cell label. Forexample, each element of the feature vector can comprise a number of amolecular label associated with the cell label. As another example, oneelement of the feature vector can comprise a number of a molecular labelassociated with the cell label, and another element of the featurevector can comprise a number of another molecular label associated withthe cell label.

At block 516, the method 500 can determine a cluster for the cell labelbased on the feature vector. In some embodiments, determining thecluster for the each cell label of the barcodes (or barcoded targets)based on the feature vector comprises clustering the each cell label ofthe barcodes (or barcoded targets) into the cluster based on a distanceof the feature vector to the cluster in a feature vector space.Determining the cluster for the each cell label of the plurality ofbarcoded targets based on the feature vector comprises: projecting thefeature vector from a feature vector space into a lower dimensionalspace; and clustering the each cell label into the cluster based on adistance of the feature vector to the cluster in the lower dimensionalspace. The lower dimensional space can be a two dimensional space.

In some embodiments, projecting the feature vector from the featurevector space into the lower dimensional space comprises projecting thefeature vector from the feature vector space into the lower dimensionalspace using a t-distributed stochastic neighbor embedding (tSNE) method.Clustering the each cell label into the cluster based on the distance ofthe feature vector to the cluster in the lower dimensional space cancomprise clustering the each cell label into the cluster based on thedistance of the feature vector to the cluster in the lower dimensionalspace using a density-based method. The density-based method cancomprises a density-based spatial clustering of applications with noise(DBSCAN) method.

At block 520, the method 500 can identify the cell label as a signalcell label or a noise cell label based on the number of the cells in thecluster and a cluster size threshold. In some embodiments, the celllabel can be identified as the signal cell label if the number of celllabels in the cluster is below the cluster size threshold. The celllabel can be identified as a noise cell label if the number of celllabels in the cluster is not below the cluster size threshold.

In some embodiments, the method comprises determining the cluster sizethreshold based on the number of cell labels of the plurality ofbarcodes (or barcoded targets). The cluster size threshold can be apercentage of the number of cell labels of the plurality of barcodedtargets. In some embodiments, determining the cluster size thresholdbased on the number of cell labels of the plurality of barcodes (orbarcoded targets). The cluster size threshold can be a percentage of thenumber of cell labels of the plurality of barcodes (or barcodedtargets). In some embodiments, the method comprises determining thecluster size threshold based on numbers of molecular labels withdistinct sequences associated with each cell label of the plurality ofbarcodes (or barcoded targets).

Distinguishing Cell Labels Associated with True Cells from Noise Cells

Disclosed herein are embodiments of a method for reliably distinguishingbetween labels (e.g., cell labels) associated with true cells and noisecells. Cell labels associated with true cells are referred to herein assignal cell labels. Noise cells are referred to herein as noise celllabels. The method may detect or identify most of the true cells (orsignal cell labels) corresponding to different cell types/clusters insome embodiments. The method may be able to automatically eliminatenoise cells that are low expressers within certain cell types, such asmonocytes and plasma.

FIG. 6A is a flow diagram showing a non-limiting exemplary method 600 afor distinguishing labels associated with true cells from noise cells.The method 600 a can be based on one or more cell label identificationor classification methods (e.g., the method 400 or 500 described withreference to FIG. 4 or 5 ). The method 600 a can improve on these celllabel identification methods in some embodiments. The method can be usedfor classifying cell labels in the Rhapsody™ pipeline.

The method 600 a comprises multiple steps or actions. At block 604, themethod 600 a includes performing (or running) a cell labelidentification method (e.g., the method 400 or 500 described withreference to FIG. 4 or 5 ) to determine a plurality of true cells (orsignal cell labels, referred to as filtered cells (A) in FIG. 6 ). Forexample, the cell label identification method can be based on a log10-transformed cumulative reads curve. The cell label identificationmethod can be used to determine the inflection point where the curvestarts to level off. For example, the major inflection point can be theseparation between true cells and noise cells.

The method 600 a can include removing noise cells by, for example,restricting to genes that are highly variable (e.g., most variable)across a majority of cells (e.g., all cells) and performing a cell labelidentification method. For example, the method 600 a can includere-running the cell label identification method, run at block 604, onmost variable genes across all cells. The method 600 a can includeidentifying highly variable genes across a majority of cells (e.g., allcells) at block 608. A cell label identification method can beperformed, at block 612, on the most variable genes identified at block608 to determine one or more true cells (or signal cell labels, whereare referred to as noise cells (B) in FIG. 6 ). To identify the highlyvariable genes, the method 600 a can optionally include:log-transforming read counts of each gene within each cell (e.g., thenumber of molecular labels with distinct sequences associated of eachgene for each cell label) to determine a gene expression. For example, aread count can be log-transformed using Equation [1] below.

log 10(count+1)  Eq. [1]

At block 608, the method 600 a can include: determining one or moremeasures or indications of the expression of each gene, such as theaverage expression (or maximum, median, or minimum expression) anddispersion (e.g., variance/mean). The method 600 a can include:assigning each gene (or expression profile of each gene) into one of aplurality of bins. For example, genes can be assigned into 20 bins basedon each gene's average (or maximum, median, or minimum) expression. Thenumber of bins can be different in different implementations. In someembodiments, the number of bins can be, or be about, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000,or a number or a range between any two of these values. In someembodiments, the number of bins can be at least, or at most, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or1000.

At block 608, the method 600 a can include: within each bin, determiningone or more measures or indications of the dispersion measures of allgenes. For example, the mean and standard deviation (STD) of thedispersion measure of all genes can be determined. The method 600 a caninclude determining the normalized dispersion measure of each geneusing, for example, Equation [2].

Normalized dispersion=(dispersion−mean)/standard deviation  Eq. [2]

At block 608, the method 600 a can include: applying one or moredifferent cutoff values to the normalized dispersion to identify geneswhose expression values are highly variable (e.g., with a variabilityabove a threshold) even when compared to genes with similar averageexpression. The number of cutoff values can be different in differentimplementations. In some embodiments, the number of cutoff values canbe, or be about, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80,90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400,500, 600, 700, 800, 900, 1000, or a number or a range between any two ofthese values. In some embodiments, the number of cutoff values can be atleast, or at most, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70,80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400,500, 600, 700, 800, 900, or 1000.

In some embodiments, the method 600 a can determine a cell as a noisecell (or a cell label or a noise cell label) if, or only if, the cell isidentified as a noise cell in a threshold number of cutoff values or athreshold percentage of all cutoff values (e.g., a minority, a majority,or all cutoff values). In some embodiments, the threshold number ofcutoff values can be, or be about, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or a number or arange between any two of these values. In some embodiments, thethreshold number of cutoff values can be at least, or at most, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or1000. In some embodiments, the threshold percentage of all cutoff valuescan be, or be about, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%,13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%,27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%,41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%,55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%,69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, 99%, 99.9%, 100%, or a number or a range between any two ofthese values. In some embodiments, the threshold percentage of allcutoff values can be at least, or at most, 1%, 2%, 3%, 4%, 5%, 6%, 7%,8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%,23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%,37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%,51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%,65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%,79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.9%, or 100%. In some embodiments,such noise cell identification can improve the accuracy of the noisecells identified (e.g., decrease the possibility of identifying a truecell as a noise cell). FIG. 7 is a non-limiting exemplary schematicillustration showing identification of the most variable genes.

Referring to FIG. 6A, at block 616, the method 600 a can include:determining or identifying true cells (or signal cell labels) that maybe mis-determined (e.g., not identified) at block 604, for example, bydetermining if there are any dropout genes. If so, the method 600 a caninclude, at block 620, running or re-running a cell label identificationmethod (e.g., the cell label identification method used at block 604 or612) to determine one or more lost true cells (or lost signal celllabels) not identified at block 604. The lost true cells determined atblock 620 are referred to as lost cells (D) in FIG. 6A. Identifyingdropout genes can include: for each gene, determining the total readcounts from all cells as well as from the clean-up cells determined atblock 625. The clean-up cells can be determined using Equation [3a] orEquation [3b], where C denotes the clean-up cells, A denotes thefiltered or true cells determined at block 604, and B denotes lose cellsdetermined at block 612.

C=set_difference (A,set_difference (A,B)  Eq. [3a]

C=A−(A−B)  Eq. [3a]

Identifying dropout genes can include: identifying the genes that havebig loses (e.g., the biggest lose) in the count from clean-up cellscompared to the count from all cells. For example, the genes withbiggest loss can be determined by plotting total counts, and finding thebest line of fit to determining the genes with large residuals (e.g.,the largest residuals), such as at least one a threshold number ofstandard deviation away from the median of residuals of all genes (seeFIGS. 8A-8B). The median can be used instead of the mean to minimize theimpact of outliers in some embodiments. The threshold number of standarddeviation can be different in different implementations. In someembodiments, the threshold number of standard deviation can be, or beabout, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4,4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, or a number or a rangebetween any two of these values. In some embodiments, the thresholdnumber of standard deviation can be at least, or at most, 0.5, 0.6, 0.7,0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2,2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7,7.5, 8, 8.5, 9, 9.5, or 10.

At block 624, the method 600 a can include combining the cells (or celllabels) identified at block 620 and block 624 to determine a final setof true cells (referred to as filtered cells F in FIG. 6 ).

FIG. 6B is a flow diagram showing another non-limiting exemplary method600 b for distinguishing labels associated with true cells from noisecells. Actions performed at blocks 604-628 in FIG. 6B can be similar tothe actions performed at corresponding blocks of method 600 a describedwith reference to FIG. 6A. The method 600 b can include, running analgorithm based on log 10-transformed cumulative reads curve and findthe inflection point where the curve starts to level off at block 604.The major inflection point is the separation between cells and noise.The method 600 b can include one or more of the following steps. Startfrom all cells, get the most variable genes using z-score cutoff ofdispersion measure of genes. Focus on the most variable genes only andrun through current algorithm to infer true cells, denote this set as B.Determine cells detected by other cell label identification methodsusing all genes in the panel but not detected by the algorithm using themost variable genes only, i.e. setdiff(A, B), are determined as noisecells. In some embodiments, to be more conservative, try multipledispersion z-cutoff values, and determine a cell as noise only if thecell is classified as noise for some, a majority, or all cutoff values.Remove the noise cells from set A and get an updated cell set, usingEquation [3a] or [3b] above.

The method 600 b can include removing noise cells by restricting togenes that are most variable or highly variable across all cells, atblock 608, and re-running the algorithm (e.g., run at block 604) atblock 612. For example, the method can include one or more of thefollowing steps. Retrieve true cells. For each gene, calculate the totalread counts of all cells as well as from cells in set C. Find the genesthat are mostly dropped out in the set C. Focus on the dropped out genesand run through the method run at block 604 to retrieve any true cellsthat may get lost, denote cells identified in this step as D.

The method 600 b can include recovering true cells that may bemis-detected or mis-determined at block 604 by checking if there are anydropout genes at block 616. If so, the method 600 b can includerestricting to the dropped out genes (also referred to asunder-represented genes) and re-running the algorithm (e.g., run atblock 604) to pick up the lost true cells at block 620. The final listof cells, F, can be determined using Equation [4].

F=union(C,D)  Equation [4]

In some embodiments, at block 632, cells from block 628 can be cleanedup or polished by removing cells that do not carry high enough number ofmolecules. For example, the minimum threshold of the molecule count canbe determined by the following rules. Step (a) Find a big gap (e.g., thebiggest gap, the second biggest gap, the third biggest gap, etc.) in thetotal molecule counts of the cells lying at the bottom quarter, anddetermine the cutoff as the value of the gap. Step (b) Find the cellswith molecule counts less than the cutoff determined at step (a), and,optionally, calculate the percent of cells removed due to low moleculecount. Step (c) Under one or both of the following two conditions, donot use the adaptive cutoff determined above, but rather use the fixedcutoff of, for example, 20 molecules: condition (i) percent of cellsremoved due to low molecule count is greater than, or at least, athreshold percentage (e.g., 20%) and/or the gap is less than a thresholdnumber (e.g., 500); and condition (ii) the biggest gap in the totalmolecule count of all cells is, for example, 1. The cells after theclean-up are part of the final set of filtered cells detected by themethod 600 b.

The fixed cutoff at step (c) can be different in differentimplementations. In some embodiments, the cutoff can be, or be about, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, or a number or arange between any two of these values. In some embodiments, the cutoffcan be at least, or at most, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70,80, 90, or 100. The threshold percentage in condition (i) can bedifferent in different implementations. In some embodiments, thethreshold percentage can be, or be about, 5%, 6%, 7%, 8%, 9%, 10%, 11%,12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%,26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%,40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, or a number or arange between any two of these values. In some embodiments, thethreshold percentage can be at least, or at most, 5%, 6%, 7%, 8%, 9%,10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%,24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%,38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, or 50%. Thethreshold number of the gap in condition (i) can be different indifferent implementations. In some embodiments, threshold number of gapcan be, or be about, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500,550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or a number or arange between any two of these values. In some embodiments, thresholdnumber of gap can be at least, or at most, 50, 100, 150, 200, 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,or a number or a range between any two of these values. The biggest gapin condition (ii) can be different in different implementations. In someembodiments, the biggest gap can be, or be about, 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or a rangebetween any two of these values. In some embodiments, the biggest gapcan be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40,50, 60, 70, 80, 90, or 100.

Disclosed herein are embodiments of a method for identifying a signalcell label. In some embodiments, the method comprises: (a) obtainingsequencing data of a plurality of first targets of cells, wherein eachfirst target is associated with a number of molecular labels withdistinct sequences associated with each cell label of a plurality ofcell labels; (b) identifying each of the cell labels as a signal celllabel or a noise cell label based on the number of molecular labels withdistinct sequences associated with each of the cell labels and anidentification threshold, such as at block 604 of method 600 a or 600 b,using the method 400 or 500; and (c) re-identifying at least one of theplurality of cell labels as a signal cell label identified as a noisecell label in (b), such as blocks 608, 612 of method 600 a or 600 b,using the method 400 or 500, or re-identifying at least one of the celllabel as a noise cell label identified as a signal cell label in (b),such as blocks 616, 620 of the method 600 a or 600 b, using the method400 or 500. Identifying each of the cell labels, re-identifying at leastone of the plurality of cells labels as a signal cell label, orre-identifying at least one of the plurality of cell labels as a noisecell label can be based on an identical cell label identification methodor different cell label identification methods of the disclosure (suchas the method 400 or 500 described with reference to FIG. 4 or FIG. 5 ).The identification threshold can comprise a cell label threshold, acluster size threshold, or any combination thereof. The method cancomprise: removing one or more cell labels of the plurality of celllabels each associated with a number of molecular labels with distinctsequences below threshold of a number of molecular labels, such as atblock 628 of method 600 b described with reference to FIG. 6A.

In some embodiments, re-identifying at least one of the plurality ofcell labels as a signal cell label identified as a noise cell label in(b) comprises: determining a plurality of second targets of theplurality of first targets each with one or more variabilityindications, amongst the plurality of first targets, above a variabilitythreshold, such as at block 608 of the method 600 a or 600 b; andre-identifying at least one of the plurality of cell labels as a signalcell label identified as a noise cell label in (b) based on, for each ofthe plurality of cell labels, the number of molecular labels withdistinct sequences associated with the plurality of second targets andthe identification threshold, such as at block 612 of the method 600 aor 600 b. The one or more variability indications of the second targetcan comprise an average, a maximum, a median, a minimum, a dispersion,or any combinations thereof, of the numbers of molecular labels withdistinct sequences associated with the second target and cell labels ofthe plurality of cell labels in the sequencing data. The one or morevariability indications of the second target can comprise a standarddeviation, a normalized dispersion, or any combinations thereof,variability indications of a subset of the plurality of second targets.The variability threshold can be smaller than or equal to the size ofthe subset of the plurality of second targets.

In some embodiments, re-identifying at least one of the plurality ofcell labels as a noise cell label identified as a signal cell label in(b) comprises: determining a plurality of third targets of the pluralityof first targets each with an association with cell labels identified asnoise cell labels in (c) above an association threshold, such as atblock 616 of the method 600 a or 600 b; and re-identifying at least oneof the cell label as a noise cell label identified as a signal celllabel in (b), for each of the plurality of cell labels, based on thenumber of molecular labels with distinct sequences associated with theplurality of third targets, and the identification threshold, such as atblock 620 of the method 600 a or 600 b. Determining the plurality ofthird targets of the plurality of first targets each with an associationwith cell labels identified as noise cell labels in (c) above theassociation threshold can comprise: determining a plurality of remainingcells labels identified as signal cell labels after re-identifying atleast one of the cell label as a signal cell label identified as a noisecell label in (b); determining the plurality of third targets based onfor each of the plurality of cell labels, the number of molecular labelswith distinct sequences associated with the plurality of targets, andfor each of the plurality of remaining cell labels, the number ofmolecular labels with distinct sequences associated with the pluralityof targets.

Sequencing

In some embodiments, estimating the number of different barcoded targets(e.g., stochastically barcoded targets) can comprise determining thesequences of the labeled targets, the spatial label, the molecularlabel, the sample label, the cell label, or any product thereof (e.g.labeled-amplicons, or labeled-cDNA molecules). An amplified target canbe subjected to sequencing. Determining the sequence of a barcodedtarget (e.g., a stochastically barcoded target) or any product thereofcan comprise conducting a sequencing reaction to determine the sequenceof at least a portion of a sample label, a spatial label, a cell label,a molecular label, at least a portion of the labeled target (e.g.,stochastically labeled target), a complement thereof, a reversecomplement thereof, or any combination thereof.

Determination of the sequence of a barcoded target or a stochasticallybarcoded target (e.g. amplified nucleic acid, labeled nucleic acid, cDNAcopy of a labeled nucleic acid, etc.) can be performed using variety ofsequencing methods including, but not limited to, sequencing byhybridization (SBH), sequencing by ligation (SBL), quantitativeincremental fluorescent nucleotide addition sequencing (QIFNAS),stepwise ligation and cleavage, fluorescence resonance energy transfer(FRET), molecular beacons, TaqMan reporter probe digestion,pyrosequencing, fluorescent in situ sequencing (FISSEQ), FISSEQ beads,wobble sequencing, multiplex sequencing, polymerized colony (POLONY)sequencing; nanogrid rolling circle sequencing (ROLONY), allele-specificoligo ligation assays (e.g., oligo ligation assay (OLA), single templatemolecule OLA using a ligated linear probe and a rolling circleamplification (RCA) readout, ligated padlock probes, or single templatemolecule OLA using a ligated circular padlock probe and a rolling circleamplification (RCA) readout), and the like.

In some embodiments, determining the sequence of the barcoded target(e.g., stochastically barcoded target) or any product thereof comprisespaired-end sequencing, nanopore sequencing, high-throughput sequencing,shotgun sequencing, dye-terminator sequencing, multiple-primer DNAsequencing, primer walking, Sanger dideoxy sequencing, Maxim-Gilbertsequencing, pyrosequencing, true single molecule sequencing, or anycombination thereof. Alternatively, the sequence of the barcoded targetor any product thereof can be determined by electron microscopy or achemical-sensitive field effect transistor (chemFET) array.

High-throughput sequencing methods, such as cyclic array sequencingusing platforms such as Roche 454, Illumina Solexa, ABI-SOLiD, IONTorrent, Complete Genomics, Pacific Bioscience, Helicos, or thePolonator platform, can be utilized. In some embodiment, sequencing cancomprise MiSeq sequencing. In some embodiment, sequencing can compriseHiSeq sequencing.

The labeled targets (e.g., stochastically labeled targets) can comprisenucleic acids representing from about 0.01% of the genes of anorganism's genome to about 100% of the genes of an organism's genome.For example, about 0.01% of the genes of an organism's genome to about100% of the genes of an organism's genome can be sequenced using atarget complimentary region comprising a plurality of multimers bycapturing the genes containing a complimentary sequence from the sample.In some embodiments, the barcoded targets comprise nucleic acidsrepresenting from about 0.01% of the transcripts of an organism'stranscriptome to about 100% of the transcripts of an organism'stranscriptome. For example, about 0.501% of the transcripts of anorganism's transcriptome to about 100% of the transcripts of anorganism's transcriptome can be sequenced using a target complimentaryregion comprising a poly(T) tail by capturing the mRNAs from the sample.

Determining the sequences of the spatial labels and the molecular labelsof the plurality of the barcodes (e.g., stochastic barcodes) can includesequencing 0.00001%, 0.0001%, 0.001%, 0.01%, 0.1%, 1%, 2%, 3%, 4%, 5%,6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 99%, 100%,or a number or a range between any two of these values, of the pluralityof barcodes. Determining the sequences of the labels of the plurality ofbarcodes, for example the sample labels, the spatial labels, and themolecular labels, can include sequencing 1, 10, 20, 30, 40, 50, 60, 70,80, 90, 100, 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³,10¹⁴, 10¹⁵, 10¹⁶, 10¹⁷, 10¹⁸, 10¹⁹, 10²⁰, or a number or a range betweenany two of these values, of the plurality of barcodes. Sequencing someor all of the plurality of barcodes can include generating sequenceswith read lengths of, of about, of at least, or of at most, 10, 20, 30,40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900,1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, or a numberor a range between any two of these values, of nucleotides or bases.

Sequencing can comprise sequencing at least or at least about 10, 20,30, 40, 50, 60, 70, 80, 90, 100 or more nucleotides or base pairs of thebarcoded targets. For example, sequencing can comprise generatingsequencing data with sequences with read lengths of 50, 75, or 100, ormore nucleotides by performing polymerase chain reaction (PCR)amplification on the plurality of barcoded targets. Sequencing cancomprise sequencing at least or at least about 200, 300, 400, 500, 600,700, 800, 900, 1,000 or more nucleotides or base pairs of the barcodedtargets. Sequencing can comprise sequencing at least or at least about1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 or morenucleotides or base pairs of the barcoded targets.

Sequencing can comprise at least about 200, 300, 400, 500, 600, 700,800, 900, 1,000 or more sequencing reads per run. In some embodiments,sequencing comprises sequencing at least or at least about 1500, 2000,3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 or more sequencingreads per run. Sequencing can comprise less than or equal to about1,600,000,000 sequencing reads per run. Sequencing can comprise lessthan or equal to about 200,000,000 reads per run.

Samples

In some embodiments, the plurality of targets can be comprised in one ormore samples. A sample can comprise one or more cells, or nucleic acidsfrom one or more cells. A sample can be a single cell or nucleic acidsfrom a single cell. The one or more cells can be of one or more celltypes. At least one of the one or more cell types can be brain cell,heart cell, cancer cell, circulating tumor cell, organ cell, epithelialcell, metastatic cell, benign cell, primary cell, circulatory cell, orany combination thereof.

A sample for use in the method of the disclosure can comprise one ormore cells. A sample can refer to one or more cells. In someembodiments, the plurality of cells can include one or more cell types.At least one of the one or more cell types can be brain cell, heartcell, cancer cell, circulating tumor cell, organ cell, epithelial cell,metastatic cell, benign cell, primary cell, circulatory cell, or anycombination thereof. In some embodiments, the cells are cancer cellsexcised from a cancerous tissue, for example, breast cancer, lungcancer, colon cancer, prostate cancer, ovarian cancer, pancreaticcancer, brain cancer, melanoma and non-melanoma skin cancers, and thelike. In some embodiments, the cells are derived from a cancer butcollected from a bodily fluid (e.g. circulating tumor cells).Non-limiting examples of cancers can include, adenoma, adenocarcinoma,squamous cell carcinoma, basal cell carcinoma, small cell carcinoma,large cell undifferentiated carcinoma, chondrosarcoma, and fibrosarcoma.The sample can include a tissue, a cell monolayer, fixed cells, a tissuesection, or any combination thereof. The sample can include a biologicalsample, a clinical sample, an environmental sample, a biological fluid,a tissue, or a cell from a subject. The sample can be obtained from ahuman, a mammal, a dog, a rat, a mouse, a fish, a fly, a worm, a plant,a fungus, a bacterium, a virus, a vertebrate, or an invertebrate.

In some embodiments, the cells are cells that have been infected withvirus and contain viral oligonucleotides. In some embodiments, the viralinfection can be caused by a virus such as single-stranded (+ strand or“sense”) DNA viruses (e.g. parvoviruses), or double-stranded RNA viruses(e.g. reoviruses). In some embodiments, the cells are bacteria. Thesecan include either gram-positive or gram-negative bacteria. In someembodiments, the cells are fungi. In some embodiments, the cells areprotozoans or other parasites.

As used herein, the term “cell” can refer to one or more cells. In someembodiments, the cells are normal cells, for example, human cells indifferent stages of development, or human cells from different organs ortissue types. In some embodiments, the cells are non-human cells, forexample, other types of mammalian cells (e.g. mouse, rat, pig, dog, cow,or horse). In some embodiments, the cells are other types of animal orplant cells. In other embodiments, the cells can be any prokaryotic oreukaryotic cells.

In some embodiments the cells are sorted prior to associating a cellwith a bead. For example the cells can be sorted byfluorescence-activated cell sorting or magnetic-activated cell sorting,or more generally by flow cytometry. The cells can be filtered by size.In some embodiments a retentate contains the cells to be associated withthe bead. In some embodiments the flow through contains the cells to beassociated with the bead.

A sample can refer to a plurality of cells. The sample can refer to amonolayer of cells. The sample can refer to a thin section (e.g., tissuethin section). The sample can refer to a solid or semi-solid collectionof cells that can be place in one dimension on an array.

Execution Environment

The present disclosure provides computer systems that are programmed toimplement methods (e.g., the method 400, the method 500, the method 600a, or the method 600 b described with reference to FIGS. 4, 5, 6A, and6B) of the disclosure. FIG. 9 shows a computer system 900 that isprogrammed or otherwise configured to implement any of the methodsdisclosed herein. The computer system 900 can be an electronic device ofa user or a computer system that is remotely located with respect to theelectronic device. The electronic device can be a mobile electronicdevice.

The computer system 900 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 905, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 900 also includes memory or memorylocation 910 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 915 (e.g., hard disk), communicationinterface 920 (e.g., network adapter) for communicating with one or moreother systems, and peripheral devices 925, such as cache, other memory,data storage and/or electronic display adapters. The memory 910, storageunit 915, interface 920 and peripheral devices 925 are in communicationwith the CPU 905 through a communication bus (solid lines), such as amotherboard. The storage unit 915 can be a data storage unit (or datarepository) for storing data. The computer system 900 can be operativelycoupled to a computer network (“network”) 930 with the aid of thecommunication interface 920. The network 930 can be the Internet, aninternet and/or extranet, or an intranet and/or extranet that is incommunication with the Internet. The network 930 in some cases is atelecommunication and/or data network. The network 930 can include oneor more computer servers, which can enable distributed computing, suchas cloud computing. The network 930, in some cases with the aid of thecomputer system 900, can implement a peer-to-peer network, which mayenable devices coupled to the computer system 900 to behave as a clientor a server.

The CPU 905 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 910. The instructionscan be directed to the CPU 905, which can subsequently program orotherwise configure the CPU 905 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 905 can includefetch, decode, execute, and writeback. The CPU 905 can be part of acircuit, such as an integrated circuit. One or more other components ofthe system 900 can be included in the circuit. In some cases, thecircuit is an application specific integrated circuit (ASIC).

The storage unit 915 can store files, such as drivers, libraries andsaved programs. The storage unit 915 can store user data, e.g., userpreferences and user programs. The computer system 900 in some cases caninclude one or more additional data storage units that are external tothe computer system 900, such as located on a remote server that is incommunication with the computer system 900 through an intranet or theInternet.

The computer system 900 can communicate with one or more remote computersystems through the network 930. For instance, the computer system 900can communicate with a remote computer system of a user (e.g., amicrobiologist). Examples of remote computer systems include personalcomputers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad,Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone,Android-enabled device, Blackberry®), or personal digital assistants.The user can access the computer system 900 via the network 930.

The computer system 900 can include or be in communication with anelectronic display 935 that comprises a user interface (UI) 940 forproviding, for example, an output indicative of string co-occurrence orinteractions of a plurality of taxa of microorganisms, as represented bystrings. Examples of UI's include, without limitation, a graphical userinterface (GUI) and web-based user interface.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 900, such as, for example, on the memory910 or electronic storage unit 915. The machine executable or machinereadable code can be provided in the form of software. During use, thecode can be executed by the processor 905. In some cases, the code canbe retrieved from the storage unit 915 and stored on the memory 910 forready access by the processor 905. In some situations, the electronicstorage unit 915 can be precluded, and machine-executable instructionsare stored on memory 910.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 900, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

In some embodiments, some or all of the analysis functionality of thecomputer system 900 can be packaged in a single software package. Insome embodiments, the complete set of data analysis capabilities cancomprise a suite of software packages. In some embodiments, the dataanalysis software can be a standalone package that is made available tousers independently of an assay instrument system. In some embodiments,the software can be web-based, and can allow users to share data. Insome embodiments, commercially-available software can be used to performall or a portion of the data analysis, for example, the Seven Bridges(https://www.sbgenomics.com/) software can be used to compile tables ofthe number of copies of one or more genes occurring in each cell for theentire collection of cells.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms or methods. A method can be implemented by wayof software upon execution by the central processing unit 905. Exemplaryapplications of algorithms or methods implemented by way of softwareinclude bioinformatics methods for sequence read processing (e.g.,merging, filtering, trimming, clustering), alignment and calling, andprocessing of string data and optical density data (e.g., most probablenumber and cultivable abundance determinations).

In an exemplary embodiment, the computer system 900 can perform dataanalysis of the sequence datasets generated by performing single cell,stochastic barcoding assays. Examples of data analysis functionalityinclude, but are not limited to, (i) algorithms fordecoding/demultiplexing of the sample label, cell label, spatial label,and molecular label, and target sequence data provided by sequencing thestochastic barcode library created in running the assay, (ii) algorithmsfor determining the number of reads per gene per cell, and the number ofunique transcript molecules per gene per cell, based on the data, andcreating summary tables, (iii) statistical analysis of the sequencedata, e.g., for clustering of cells by gene expression data, or forpredicting confidence intervals for determinations of the number oftranscript molecules per gene per cell, etc., (iv) algorithms foridentifying sub-populations of rare cells, for example, using principalcomponent analysis, hierarchical clustering, k-mean clustering,self-organizing maps, neural networks etc., (v) sequence alignmentcapabilities for alignment of gene sequence data with known referencesequences and detection of mutation, polymorphic markers and splicevariants, and (vi) automated clustering of molecular labels tocompensate for amplification or sequencing errors. In some embodiments,the computer system 900 can output the sequencing results in usefulgraphical formats, e.g., heatmaps that indicate the number of copies ofone or more genes occurring in each cell of a collection of cells. Insome embodiments, the computer system 900 can execute algorithms forextracting biological meaning from the sequencing results, for example,by correlating the number of copies of one or more genes occurring ineach cell of a collection of cells with a type of cell, a type of rarecell, or a cell derived from a subject having a specific disease orcondition. In some embodiment, the computer system 900 can executealgorithms for comparing populations of cells across differentbiological samples.

EXAMPLES

Some aspects of the embodiments discussed above are disclosed in furtherdetail in the following examples, which are not in any way intended tolimit the scope of the present disclosure.

Example 1 Separation of Signal Cell Labels and Noise Cell Labels—SecondDerivatives

This example describes separating signal cell labels (also referred toas true cell labels) and noise cell labels based on the number of reads(or molecules) associated with the cell labels.

In some instances, noise cell labels may have fewer reads (or molecules)associated with them than signal cell labels. For example, noise celllabels can be caused by cells paired with no bead being lysed and theirnucleic acid contents diffusing and associating with beads not pairedwith any cells. This type of noise cell labels can contain a portion oftotal nucleic acid contents of a cell. Thus, molecules from the samecell can appear to be from two different cells (e.g., as if they werefrom two different beads because the cell label has mutated).

As another example, noise cell labels can be caused by mutations duringthe manufacturing process of the beads. Also, noise cell labels can becaused by insufficient exonuclease treatment (e.g., at steps 216 shownin FIG. 2 ) such that single stranded DNA on the bead can hybridize andform PCR chimeras during the PCR process. These two types of noise celllabels can occur randomly and rarely.

FIG. 10 shows a non-limiting exemplary cumulative sum plot. Cumulativenumber of reads versus sorted cell label index on log-log scale. The redline shows the cutoff between true cell labels and noise cell labels. InFIG. 10 , a sudden slope change in the cumulative number of reads (ormolecules) was observed when sorting all cell labels based on number ofreads. To find the cutoff between true cell labels and noise celllabels, the second derivatives of the log-log plot were calculated. FIG.11 shows a non-limiting second derivative plot of the cumulative sumplot in FIG. 10 . Second derivatives of log 10-transformed cumulativenumber of reads versus log 10-transformed sorted cell label index. Theglobal minimum was inferred as the cutoff between true cell labels andnoise cell labels.

In some embodiments, the cell number inferred may not agree with thecell number input and cell number observed in the image analysis.Instead, the cutoff determined using FIG. 11 might either reflect theseparation between signal cells of high and low expressing levels, orthe separation between different types of noise labels. To correctlyinfer cell number in these cases, a constraint of percentage of reads(or molecules) in signal cell labels is set in the range of 45% to 92%,based on the empirical data. The number of cells observed from imageanalysis can be set as a constraint optionally when this value isavailable.

Altogether, these data demonstrate identifying true cell labels (alsoreferred to as signal cell labels) and noise cell labels can be achievedby determining a minimum of a second derivative plot which correspondsto a cell label threshold for distinguishing true cell labels and noisecell labels.

Example 2 Separation of Signal Cell Labels and Noise CellLabels—Clustering

This example describes separating signal cell labels (also referred toas true cell labels) and noise cell labels based on their expressionpatterns (also referred to as feature vectors).

In some embodiments, samples for stochastic barcoding experiments cancontain cell types with wide range of expression levels. In suchexperiments, some cell types could have very similar number of moleculesto noise cell labels. To separate true cell labels from noise celllabels when numbers of molecules associated are indistinguishable,clustering techniques can be used to classify noise cell labels and eachcell type with low expression level. The method can be based on theassumption that cell labels within the same cell type would have moresimilar expression patterns than cell labels between different celltypes, and noise cell labels would also have more similar featurevectors to each other than to true cell labels.

FIG. 12 shows a non-limiting tSNE plot of signal or noise cell labels.PBMC cell were stochastically barcoded. The 5450 cell labels in FIG. 12contained 240 true cell labels with low expression levels and 5210 noisecell labels. In particular, the classification was done by firstprojecting the expression vectors into a two dimensional (2D) spaceusing a t-distributed stochastic neighbor embedding (tSNE), andclustering the 2D coordinates by a density-based spatial clustering ofapplications with noise (DBScan) method. With the knowledge that most ofthe 5450 cell labels are noise cell labels, the dominant cluster wasconcluded to be noise label cluster, and the other three compactclusters were concluded to be true cell labels of three different celltypes.

Altogether, these data demonstrate that identifying true cell labels andnoise cell labels can be achieved by clustering of expression patternsassociated with the cell labels.

Example 3 Identification of True Cells and Noise Cell Labels—SecondDerivatives

This example describes separating true cells (also referred to as signalcell labels or true cell labels) and noise (also referred to as noisecells or noise cell labels) based on the number of reads (or molecules)associated with the cells (or cell labels).

Example dataset 1. This dataset was processed using the BD™ BreastCancer gene panel (BrCa400) with three distinct breast cancer cell linesand donor isolated PBMCs (Peripheral Blood Mononuclear Cells). Themethod 400 b, described with reference to FIG. 4 , identified 8017cells, among which 186 cells were identified as noise cells by themethod 600 a, described with reference to FIG. 6 . The method 600 adetected additional 1263 cells, which were confirmed to be mostly PBMCs,see FIGS. 13A-13B, 14A-14D. FIGS. 13A-13B are non-limiting exemplaryplots illustrating comparison of cells identified by the method 400illustrated with reference to FIG. 4 (FIG. 13A) and the method 600 aillustrated with reference to FIG. 6A (FIG. 13B) for a sample processedusing the BD™ Breast Cancer gene panel with three distinct breast cancercell lines and donor isolated PBMCs. The dots labeled as blue in bothFIGS. 13A-13B are the common cells detected by both methods. The dotslabeled as red in FIG. 13A are the cells identified as noise by method600 a. The dots labeled as red in FIG. 13B are the additional true cellsidentified by method 600 a. FIG. 14A is non-limiting exemplary plotshowing the cells identified by the method 600 a, where the cellslabeled red are the additional cells identified (compared to the cellsidentified by method 400 illustrated with reference to FIG. 4 ). Thecells are colored by expression of PBMCs, such as B cells (FIG. 14B), NKcells (FIG. 14C), and T cells (FIG. 14D). FIGS. 14B-14D show that theadditional cells identified by the method 600 a are indeed true cells.

Example dataset 2. This dataset was processed using the BD™ Blood genepanel (Blood500) with a healthy donor isolated PBMCs. The method 400 b,described with reference to FIG. 4 , identified 13,950 cells, amongwhich 1,333 cells were identified as noise cells by the method 600 a,described with reference to FIG. 6 . The method 600 a detectedadditional 3,842 cells, which were confirmed to be mostly T cells, aswell as expressed in important genes such as LAT (Linker for Activationof T cells) and IL7R (Interleukin 7 Receptor), see FIGS. 15A-15B,16A-16B, and 17A-17D. FIG. 15A-15B are non-limiting exemplary plotsillustrating comparison of cells identified by the method 400illustrated with reference to FIG. 4 (FIG. 15A) and the method 600 aillustrated with reference to FIG. 6A (FIG. 15B) for a sample processedusing the BD™ Blood gene panel with a healthy donor isolated PBMCs. Thedots labeled as blue in both FIGS. 15A-15B are the common cells detectedby both methods. The dots labeled as red in FIG. 15A are the cellsidentified as noise by the method 600 a. The dots labeled as red in FIG.15B are the additional cells identified by the method 600 a. FIG.16A-16B are non-limiting exemplary plots showing the cells identified bythe method 400. In FIG. 16A, the cells labeled red are the cellsidentified as noise by the method 600 a. In FIG. 16B, the cells arecolored by expression of a group of Monocyte marker genes, such as CD14and S100A6. The “noise” cells identified by the improved algorithm weremostly low expressers of the Monocytes. FIG. 17A is a non-limitingexemplary plot showing the cells identified by the method 600 a, wherethe cells labeled red are the additional cells identified. The cells arecolored by expression of T cells (FIG. 17B), expression of importantgenes LAT (FIG. 17C) and IL7R (FIG. 17D).

Altogether, the data that different embodiments of the method ofidentifying signal cell labels or true cells have different performanceand may be complementary to one another.

In at least some of the previously described embodiments, one or moreelements used in an embodiment can interchangeably be used in anotherembodiment unless such a replacement is not technically feasible. Itwill be appreciated by those skilled in the art that various otheromissions, additions and modifications may be made to the methods andstructures described above without departing from the scope of theclaimed subject matter. All such modifications and changes are intendedto fall within the scope of the subject matter, as defined by theappended claims.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. As used in this specification and the appended claims, thesingular forms “a,” “an,” and “the” include plural references unless thecontext clearly dictates otherwise. Any reference to “or” herein isintended to encompass “and/or” unless otherwise stated.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations. In addition, even if a specificnumber of an introduced claim recitation is explicitly recited, thoseskilled in the art will recognize that such recitation should beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, means at leasttwo recitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,etc.” is used, in general such a construction is intended in the senseone having skill in the art would understand the convention (e.g., “asystem having at least one of A, B, and C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). In those instances where a convention analogous to “atleast one of A, B, or C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one of A, B, or C” wouldinclude but not be limited to systems that have A alone, B alone, Calone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). It will be further understood by those withinthe art that virtually any disjunctive word and/or phrase presenting twoor more alternative terms, whether in the description, claims, ordrawings, should be understood to contemplate the possibilities ofincluding one of the terms, either of the terms, or both terms. Forexample, the phrase “A or B” will be understood to include thepossibilities of “A” or “B” or “A and B.”

In addition, where features or aspects of the disclosure are describedin terms of Markush groups, those skilled in the art will recognize thatthe disclosure is also thereby described in terms of any individualmember or subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, such as in terms of providing a written description, allranges disclosed herein also encompass any and all possible sub-rangesand combinations of sub-ranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed herein canbe readily broken down into a lower third, middle third and upper third,etc. As will also be understood by one skilled in the art all languagesuch as “up to,” “at least,” “greater than,” “less than,” and the likeinclude the number recited and refer to ranges which can be subsequentlybroken down into sub-ranges as discussed above. Finally, as will beunderstood by one skilled in the art, a range includes each individualmember. Thus, for example, a group having 1-3 articles refers to groupshaving 1, 2, or 3 articles. Similarly, a group having 1-5 articlesrefers to groups having 1, 2, 3, 4, or 5 articles, and so forth.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

1.-65. (canceled)
 66. A method for identifying a signal cell label,comprising: (a) barcoding a plurality of targets in a plurality of cellsusing a plurality of barcodes to create a plurality of barcoded targets,wherein each of the plurality of barcodes comprises a cell label and amolecular label, wherein barcoded targets created from targets ofdifferent cells of the plurality of cells have different cell labels,and wherein barcoded targets created from targets of the same cell ofthe plurality of cells have different molecular labels; (b) obtainingsequencing data of the plurality of barcoded targets; (c) determining afeature vector of each cell label of the plurality of barcoded targets,wherein the feature vector comprise numbers of molecular labels withdistinct sequences associated with the each cell label; (d) determininga cluster for the each cell label of the plurality of barcoded targetsbased on the feature vector; and (e) identifying the each cell label ofthe plurality of barcoded targets as a signal cell label or a noise celllabel based on a number of cell labels in the cluster and a cluster sizethreshold. 67.-80. (canceled)
 81. A method for identifying a signal celllabel, comprising: (a) obtaining sequencing data of a plurality ofbarcoded targets, wherein the plurality of barcoded targets is createfrom a plurality of targets in a plurality of cells that are barcodedusing a plurality of barcodes, wherein each of the plurality of barcodescomprises a cell label and a molecular label, wherein barcoded targetscreated from targets of different cells of the plurality of cells havedifferent cell labels, and wherein barcoded targets created from targetsof the same cell of the plurality of cells have different molecularlabels; (b) determining a feature vector of each cell label of theplurality of barcoded targets, wherein the feature vector comprisenumbers of molecular labels with distinct sequences associated with theeach cell label; (c) determining a cluster for the each cell label ofthe plurality of barcoded targets based on the feature vector; and (d)identifying the each cell label of the plurality of barcoded targets asa signal cell label or a noise cell label based on a number of celllabels in the cluster and a cluster size threshold.
 82. The method ofclaim 81, wherein determining the cluster for the each cell label of theplurality of barcoded targets based on the feature vector comprisesclustering the each cell label of the plurality of barcoded targets intothe cluster based on a distance of the feature vector to the cluster ina feature vector space.
 83. The method of claim 81, wherein determiningthe cluster for the each cell label of the plurality of barcoded targetsbased on the feature vector comprises: projecting the feature vectorfrom a feature vector space into a lower dimensional space; andclustering the each cell label into the cluster based on a distance ofthe feature vector to the cluster in the lower dimensional space. 84.The method of claim 83, wherein the lower dimensional space is a twodimensional space.
 85. The method of claim 83, wherein projecting thefeature vector from the feature vector space into the lower dimensionalspace comprises projecting the feature vector from the feature vectorspace into the lower dimensional space using a t-distributed stochasticneighbor embedding (tSNE) method.
 86. The method of claim 83, whereinclustering the each cell label into the cluster based on the distance ofthe feature vector to the cluster in the lower dimensional spacecomprises clustering the each cell label into the cluster based on thedistance of the feature vector to the cluster in the lower dimensionalspace using a density-based method.
 87. The method of claim 86, whereinthe density-based method comprises a density-based spatial clustering ofapplications with noise (DBSCAN) method.
 88. The method of claim 83,wherein the cell label is identified as the signal cell label if thenumber of cell labels in the cluster is below the cluster sizethreshold.
 89. The method of claim 83, wherein the cell label isidentified as a noise cell label if the number of cell labels in thecluster is not below the cluster size threshold.
 90. The method of claim83, comprising determining the cluster size threshold based on thenumber of cell labels of the plurality of barcoded targets.
 91. Themethod of claim 90, wherein the cluster size threshold is a percentageof the number of cell labels of the plurality of barcoded targets. 92.The method of claim 83, comprising determining the cluster sizethreshold based on the number of cell labels of the plurality ofbarcoded targets.
 93. The method of claim 92, wherein the cluster sizethreshold is a percentage of the number of cell labels of the pluralityof barcoded targets.
 94. The method of claim 83, comprising determiningthe cluster size threshold based on numbers of molecular labels withdistinct sequences associated with each cell label of the plurality ofbarcoded targets.
 95. The method of claim 83, comprising: (e) for one ormore of the plurality of targets: 1counting the number of molecularlabels with distinct sequences associated with the target in thesequencing data; and 2) estimating the number of the target based on thenumber of molecular labels with distinct sequences associated with thetarget in the sequencing data counted in (1).
 96. A method foridentifying a signal cell label, comprising: (a) obtaining sequencingdata of a plurality of first targets of cells, wherein each first targetis associated with a number of molecular labels with distinct sequencesassociated with each cell label of a plurality of cell labels; (b)identifying each of the cell labels as a signal cell label or a noisecell label based on the number of molecular labels with distinctsequences associated with each of the cell labels and an identificationthreshold; and (c) re-identifying at least one of the plurality of celllabels as a signal cell label identified as a noise cell label in (b) orre-identifying at least one of the cell label as a noise cell labelidentified as a signal cell label in (b). 97.-104. (canceled)
 105. Acomputer system for determining the number of targets comprising: ahardware processor; and non-transitory memory having instructions storedthereon, which when executed by the hardware processor causes theprocessor to perform the method of claim
 81. 106. A computer readablemedium comprising code for performing the method for performing themethod of claim 81.